0.0.1 Descriptive Statistics

Labour MPs and Intakes
General Election Total MPs Labour MPs Female Labour MPs Labour MPs Intake Intake Women Intake Short list Nominated Short list
1997 659 418 101 (24%) 177 64 (36%) 35 38
2001 659 412 95 (23%) 38 4 (11%) 0 0
2005 646 355 98 (28%) 40 26 (65%) 23 30
2010 650 258 81 (31%) 64 32 (50%) 28 63
2015 650 232 99 (43%) 49 31 (63%) 31 77

Data in this table is from House of Commons library reports (Audickas, Hawkins, & Cracknell, 2017; Kelly, 2016). All women short lists were not used by Labour during the 2001 General Election.

Number of Speeches and Words in Dataset
Gender Speeches Words
All 656,412 111,180,398
Female 148,702 26,231,034
Male 507,710 84,949,364
Conservatives
All 285,291 44,800,169
Female 48,768 7,363,031
Male 236,523 37,437,138
Labour
All 261,942 46,494,850
Female 84,569 15,897,929
Non-All Women Shortlists 28,695 5,422,776
All Women Shortlists 55,874 10,475,153
Male 177,373 30,596,921
Liberal Democrat
All 72,716 13,485,902
Female 7,552 1,503,459
Male 65,164 11,982,443
Other
All 36,463 6,399,477
Female 7,813 1,466,615
Male 28,650 4,932,862

1 Methodology

Previous research on gender differences in political speech patterns has focused on differences between male and female politicians (Yu, 2014) or on variations in Hilary Clinton’s speech patterns (Bligh, Merolla, Schroedel, & Gonzalez, 2010; Jones, 2016). This paper focuses on differences in speech patterns between female Labour MPs nominated through All Women Shortlists (AWS) and female Labour MPs nominated through open short lists. We examined differences in speaking styles using the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker, Boyd, Jordan, & Blackburn, 2015) and the spaCy (Honnibal & Montani, 2017) Parts-of-Speech (POS) tagger. We examined differences in the topics discussed by AWS and non-AWS MPs, using \({\chi}^2\) tests for individual words and for bigrams. We trained a Naive Bayes classifier to distinguish AWS and non-AWS speeches. We used structured topic models (STM) to identify the topics discussed by AWS and non-AWS MPs.

To account for the possible effects of age, parliamentary experience and cohort, and in order to compare women selected through all women short lists to women who were not (but who theoretically had the opportunity to contest all-women short lists), our analysis is been restricted only to Labour MPs first elected to the House of Commons in the 1997 General Election, up to but excluding the 2017 General Election. Comparisons between MPs of different parties are also restricted to MPs first elected in the 1997 General Election, and before the 2017 General Election. Speeches made by the Speaker, including Deputy Speakers, were also excluded. Words contained in parentheses were removed, as they are added by Hansard to provide additional information not actually spoken by the MP.1 Speeches and data on MPs’ gender and party affiliation are from a previously assembled dataset (Odell, 2018). Information on candidates selected through all women short lists is from the House of Commons Library (Kelly, 2016). Unsuccessful General Election candidates selected through all women short lists who were subsequently elected in a byelection are classified as having been selected on an all women short list.

1.1 Linguistic Inquiry and Word Coun

Word classification used the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker et al., 2015) and tokenising tools from the Quanteda R package (Benoit, 2018). Word counts and words-per-sentence were calculated using stringi (Gagolewski, 2018), a wrapper to the ICU regex library.

Following Yu (2014) drawing on Newman, Groom, Handelman, & Pennebaker (2008) we used the following LIWC categories:

  • All Pronouns (pronoun)
  • First person singular pronouns (i)
  • First person plural pronouns (we)
  • Verbs (verb)
  • Auxiliary verbs (auxverb)
  • Social processes (social)
  • Positive emotions (posemo)
  • Negative emotions (negemo)
  • Tentative words (tentat)
  • Articles (article)
  • Prepositions (preps)
  • Anger words (anger)
  • Swear words (swear)
  • Cognitive processes (cogproc)
  • Words longer than six letters (Sixltr)

We also included mean words-per-sentence (WPS), total speach word count (WC) and Flesch–Kincaid grade level (FK) (Kincaid, Fishburne, Rogers, & Chissom, 1975), calculated using Quanteda (Benoit, 2018) and stringi (Gagolewski, 2018).

1.2 Women vs Men

Effect Sizes for Male and Female Labour MPs
Women
Men
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.07 4.60 10.15 4.99 0.02 negligible
First person singular pronouns 1.89 2.41 2.02 2.55 0.05 negligible
First person plural pronouns 0.97 1.42 0.99 1.51 0.01 negligible
Verbs 12.82 5.00 12.67 5.36 -0.03 negligible
Auxiliary verbs 7.91 3.45 7.93 3.69 0.01 negligible
Social processes 8.47 4.82 8.18 5.11 -0.06 negligible
Positive emotions 2.73 2.49 2.57 2.54 -0.06 negligible
Negative emotions 1.15 1.68 1.07 1.77 -0.05 negligible
Tentative words 1.48 1.74 1.58 1.90 0.05 negligible
More than six letters 10.58 3.68 10.22 3.92 -0.10 negligible
Articles 7.65 3.30 7.96 3.55 0.09 negligible
Prepositions 12.58 4.42 12.14 4.73 -0.10 negligible
Anger words 0.23 0.81 0.24 0.77 0.01 negligible
Swear words 0.00 0.06 0.00 0.09 0.01 negligible
Cognitive processes 8.68 4.83 8.82 5.15 0.03 negligible
Words per Sentence 43.63 19.68 41.15 20.04 -0.13 negligible
Total Word Count 402.79 691.27 370.18 647.36 -0.05 negligible
Flesh-Kincaid Grade Level 10.81 7.68 9.78 7.87 -0.13 negligible

There are no categories where gender differences meet the effect size threshold of \(|0.2|\) suggested by Cohen (1988, pp. 25–26) to indicate a small effect. 4 categories – words with more than six letters, prepositions, words-per-sentence and Flesh-Kincaid grade level – exceeded the \(|0.1|\) threshold suggested by Newman et al (2008).

1.3 Short lists vs Non-Short lists

The following plots show changes in the occurences of selected LIWC terms, words-per-sentence, total word count and Flesch–Kincaid grade level, over the course of an MP’s career. There do not appear to be any notable changes in speaking style over the course of female Labour MPs’ careers.

\label{sl-key-variables}Occurence of selected LIWC terms

Occurence of selected LIWC terms

Effect Sizes for Female Labour MPs by selection process
All Women Short lists
Open Shorlists
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.01 4.67 10.19 4.48 -0.04 negligible
First person singular pronouns 1.86 2.41 1.95 2.42 -0.04 negligible
First person plural pronouns 0.88 1.36 1.16 1.51 -0.19 negligible
Verbs 12.88 5.10 12.69 4.80 0.04 negligible
Auxiliary verbs 7.94 3.49 7.86 3.38 0.02 negligible
Social processes 8.48 4.94 8.46 4.59 0.00 negligible
Positive emotions 2.69 2.52 2.81 2.42 -0.05 negligible
Negative emotions 1.16 1.69 1.13 1.67 0.02 negligible
Tentative words 1.48 1.75 1.49 1.73 0.00 negligible
More than six letters 10.52 3.73 10.70 3.58 -0.05 negligible
Articles 7.69 3.38 7.55 3.15 0.04 negligible
Prepositions 12.55 4.54 12.63 4.15 -0.02 negligible
Anger words 0.23 0.78 0.24 0.88 -0.01 negligible
Swear words 0.00 0.06 0.00 0.05 0.01 negligible
Cognitive processes 8.59 4.90 8.86 4.69 -0.06 negligible
Words per Sentence 44.02 20.45 42.85 18.05 0.06 negligible
Total Word Count 401.77 704.40 404.78 664.97 0.00 negligible
Flesh-Kincaid Grade Level 10.97 7.97 10.48 7.06 0.07 negligible

There are no categories among female Labour MPs by selection process meeting the \(|0.2|\) threshold. Only one category – first person plural pronouns, d=0.19 – exceeded \(|0.1|\).

1.4 Conservatives vs Labour

Effect Sizes for All Labour and Conservative MPs
Labour
Conservatives
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.12 4.87 10.62 4.84 0.10 negligible
First person singular pronouns 1.98 2.51 2.15 2.56 0.07 negligible
First person plural pronouns 0.98 1.48 1.22 1.70 0.15 negligible
Verbs 12.72 5.24 12.93 5.13 0.04 negligible
Auxiliary verbs 7.92 3.61 8.17 3.58 0.07 negligible
Social processes 8.28 5.02 8.13 4.80 -0.03 negligible
Positive emotions 2.62 2.53 2.85 2.66 0.09 negligible
Negative emotions 1.10 1.74 1.05 1.78 -0.03 negligible
Tentative words 1.55 1.85 1.57 1.88 0.01 negligible
More than six letters 10.34 3.85 10.28 3.76 -0.02 negligible
Articles 7.86 3.48 7.82 3.45 -0.01 negligible
Prepositions 12.28 4.64 12.38 4.49 0.02 negligible
Anger words 0.24 0.78 0.24 0.82 0.01 negligible
Swear words 0.00 0.08 0.00 0.10 0.00 negligible
Cognitive processes 8.77 5.05 8.86 5.06 0.02 negligible
Words per Sentence 41.95 19.96 42.76 20.16 0.04 negligible
Total Word Count 380.71 662.03 335.54 592.41 -0.07 negligible
Flesh-Kincaid Grade Level 10.12 7.82 10.41 7.91 0.04 negligible

There are no categories with effect sizes exceeding \(|0.2|\) between Labour and Conservative MPs, like inter-Labour differences.

1.5 All MPs Gender Differences

There are no categories with effect sizes exceeding \(|0.2|\) when comparing all male and female MPs elected from 1997 onwards. There is only one category, “Articles”, with an effect size of 0.11, greater than the \(|0.1|\) threshold suggested by Newman et al. (2008).

Effect Sizes for Male and Female MPs, All Parties
Women
Men
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.31 4.65 10.26 4.90 -0.01 negligible
First person singular pronouns 1.99 2.45 2.00 2.52 0.00 negligible
First person plural pronouns 1.11 1.57 1.08 1.59 -0.02 negligible
Verbs 12.88 4.97 12.80 5.26 -0.02 negligible
Auxiliary verbs 8.00 3.45 8.08 3.64 0.02 negligible
Social processes 8.45 4.77 8.00 4.93 -0.09 negligible
Positive emotions 2.84 2.53 2.69 2.58 -0.06 negligible
Negative emotions 1.10 1.65 1.08 1.78 -0.01 negligible
Tentative words 1.47 1.73 1.61 1.91 0.08 negligible
More than six letters 19.73 6.94 19.25 7.18 -0.07 negligible
Articles 7.62 3.31 8.00 3.51 0.11 negligible
Prepositions 12.58 4.36 12.22 4.62 -0.08 negligible
Anger words 0.23 0.78 0.25 0.82 0.02 negligible
Swear words 0.00 0.05 0.00 0.10 0.01 negligible
Cognitive processes 8.67 4.79 8.93 5.12 0.05 negligible
Words per Sentence 43.25 19.45 42.06 20.12 -0.06 negligible
Total Word Count 377.31 648.92 358.13 623.49 -0.03 negligible
Flesh-Kincaid Grade Level 10.63 7.61 10.16 7.89 -0.06 negligible

1.6 POS Analysis

Part-of-Speech Effect Sizes for Male and Female Labour MPs
Women
Men
Effect Size
Word Type Mean SD Mean SD Cohen’s D Magnitude
All Nouns 22.18 9.60 21.66 10.96 -0.05 negligible
Plural Nouns 5.85 3.72 5.03 3.79 -0.22 small
Singular Nouns 15.62 9.84 16.01 11.19 0.04 negligible
Adjectives 9.58 4.78 9.28 5.29 -0.06 negligible
Adverbs 4.91 4.26 5.07 4.91 0.03 negligible
Verbs 20.94 9.52 20.78 10.28 -0.02 negligible
Part-of-Speech Effect Sizes for AWS and non-AWS Labour MPs
All Women Short lists
Open Shorlists
Effect Size
Word Type Mean SD Mean SD Cohen’s D Magnitude
All Nouns 22.16 8.78 22.18 10.00 -0.04 negligible
Plural Nouns 6.03 3.60 5.76 3.77 -0.16 negligible
Singular Nouns 15.51 8.97 15.67 10.26 0.02 negligible
Adjectives 9.83 4.59 9.45 4.86 -0.02 negligible
Adverbs 4.95 3.78 4.89 4.49 0.03 negligible
Verbs 20.88 9.04 20.97 9.76 -0.02 negligible

Part-of-speech (POS) tagging was done using spaCy (Honnibal & Montani, 2017) and the spacyr package (Benoit & Matsuo, 2018). There is one small gender difference (d = \(|0.22|\)) in the use of plural nouns, which make up 5.85% of the words used by female Labour MPs, compared to 5.03% of words spoken by male Labour MPs. As with LIWC, there are no categories where d >= \(|0.2|\) when comparing female Labour MPs by selection process.

1.7 Tokenising / Keyness

The most commonly used words by both men and women would be protocol decorum expressions, so we calculate the keyness of words to identify gender differences in the choices of topics raised by men and women, and by short-list and non-short list women.

1.7.1 Men vs Women

Keyness – a linguistic measure of the frequency of different words in two groups of texts – reveals clear gender differences in the most disproportionately common words used by female and male Labour MPs. Unsurprisingly, despite male MPs saying almost twice as many words (30,596,921 vs 15,897,929) as their female colleagues, female Labour MPs were more than two-and-a-half (2.61) times as likely to say “women”. They were also much more likely to use “women’s” and “woman” in parliamentary debate. Female Labour MPs also appear much more likely to discuss “children”, “people”, “care”, “families”, “home”, “parents”, “work” and social policy areas such as “services”, “disabled [people]” and “housing” than their male colleagues. Male MPs were more likely to refer to military topics (“Iraq”, “nuclear”), and to parliamentary process and protocol – “question”, “political”, “conservative”, “electoral”, “house”, “party”, “argument” “liberal” and “point” are far more common in speeches by male Labour MPs than by female ones. This could suggest that male Labour MPs are more comfortable using the traditional language of House of Commons debate, and are more concerned with the rules, procedures and processes of the parliamentary system than their female colleagues.

\label{gender-keyness}Keyness between Labour MPs, by Gender

Keyness between Labour MPs, by Gender

1.7.2 Short lists vs Non-Short lists

Keyness differences by selection process are not as obviously stereotypical. Nonetheless, the most common words amongst AWS MPs included “carers”, “disabled”, “bedroom” and “sen”2. Also of note is AWS MPs making more references to their “constituency” and its “constituents”, suggesting that AWS MPs may draw on the fact they were elected by their constituents as a source political legitimacy, at least more than non-AWS MPs.

\label{sl-keyness}Keyness between Female Labour MPs, by Selection Process

Keyness between Female Labour MPs, by Selection Process

1.7.3 Labour vs Conservative

The keyness differences between Labour and Conservative MPs are much greater than gender differences within Labour. The very high use of “Lady” by Conservative MPs is reflective of the greater proportion of female MPs in other parties, as it is often used to refer to comments by other members of the house. It may also represent a greater use of traditional hosue decorum by Conservative MPs.

\label{party-keyness}Keyness between Labour and Conservative MPs

Keyness between Labour and Conservative MPs

1.8 Bigrams

We created bigrams of all first person plural and singular pronouns for female Labour MPs. As above, AWS MPs are far more likely to make references to their constituency or their constituents.

\label{bigrams-keyness}Bigram Keyness in Female Labour MPs by Selection Process

Bigram Keyness in Female Labour MPs by Selection Process

1.9 Naive Bayes classification

We trained a Naive Bayes classifier with document-frequency priors and a multinomial distribution to predict the gender of speakers when given speeches by all Labour MPs in our dataset, and the selection process when only given female Labour MPs. The accuracy of both models were roughly equivalent, 70.55% accuracy when predicting gender and 70.84% when predicting short lists. By contrast, the classifier could distinguish between Labour and Conservative speeches with 74.23% accuracy.

1.10 Topic Models

Using topic models to classify text is widely used in social sciences (Grimmer & Stewart, 2013), as, when combined with the large volume of plain text data available, it allows for a rapid and consistent method of analysis . Topic modelling and other statistic methods of textual analysis are not a substitute for reading the texts themselves, but can augment other analysis or – as in this case – analyse and classify larger amounts of text than would be feasible using human coders (Grimmer & Stewart, 2013). Topic models classify a series of documents (in this case individual speeches) into one of a given number of topics, identifying terms that are common in some documents but rare in others. When developing topic models, there is a trade-off between high precision in the classification of each document with broader topics when using smaller numbers of topics, or lower precision in individual speech classification with more finely-grained topics when using larger numbers of topics. Grimmer & Stewart (2013) also highlight the importance of validating unsurpervised topic models when applied to new sets of texts.

The R package stm (Roberts, Stewart, & Tingley, 2018) implements a structured topic model (STM) (Arora et al., 2013; Roberts, Stewart, & Airoldi, 2016). An STM incorporates data about the writer or speaker into the topic classification algorithm. This differs from traditional topic modelling methods using latent variables to identify topics (e.g. with latent Dirichlet allocation Blei, Ng, & Jordan, 2003), and then comparing proportions of each topic to one or more external variables. STM allows us to incorporate the variables we are interested in to the topic model itself, i.e. the proportion of speechs classified as belonging to each topic can vary as a function of the AWS variable.

We incorporated the AWS status of speakers into our topic model, using all speeches by female Labour MPs, with their AWS status as a covariate in classifying topics. We then matched these topics to speechs by male Labour MPs.

We produced two different structured topic model implementations, with different numbers of topics (K). For each implementation, we created Fruchterman-Reingold (Fruchterman & Reingold, 1991) diagrams. Larger vertices indicate more common topics, and the plot uses a colour scale to indicate the proportion of speeches classed in that topic made by AWS and non-AWS female Labour MPs. The space between vertices indicate the closeness of two topics.

1.10.1 Short lists vs Non-Short lists - K69

The first implementation used an algorithm developed by Lee & Mimno (2014), implemented in the stm package (Roberts et al., 2018), to estimate the number of topics across all speeches made by female Labour MPs, using the “spectral” method developed by Arora et al. (2013), implemented by Roberts et al. (2018). The resulting topic model has 69 topics, across 81,607 documents and a dictionary of 115,477 words. However, the topic quality with K = 69 is poor, and several topics have poor semantic coherence (see ).

There are several clusters of topics in . For instance, we can see the closeness of Topic 15 (economics and government budgets) and Topic 43 (housing), as both include discussions of budgets and costs, while Topics 23 (bill clauses and admendments) and 16 (education) are very far apart.

\label{k69-network}Fruchterman-Reingold plot of K69 Network

Fruchterman-Reingold plot of K69 Network

\label{k69-coherence}Coherence of K69 Topic Models

Coherence of K69 Topic Models

Count and Distribution of Topics – K69
Topic Number AWS Speeches Percent of AWS Speeches Non-AWS Speeches Percent of non-AWS Speeches Male MP Speeches Percent of Male MP Speeches
Topic 1 1,272 2.37% 353 1.27% 3,434 2.03%
Topic 2 334 0.62% 127 0.46% 1,091 0.64%
Topic 3 241 0.45% 71 0.25% 427 0.25%
Topic 4 550 1.02% 133 0.48% 835 0.49%
Topic 5 826 1.54% 206 0.74% 2,452 1.45%
Topic 6 978 1.82% 915 3.28% 4,060 2.4%
Topic 7 648 1.21% 236 0.85% 1,770 1.05%
Topic 8 70 0.13% 25 0.09% 125 0.07%
Topic 9 265 0.49% 309 1.11% 862 0.51%
Topic 10 1,024 1.91% 513 1.84% 1,065 0.63%
Topic 11 940 1.75% 580 2.08% 3,793 2.24%
Topic 12 313 0.58% 319 1.14% 1,309 0.77%
Topic 13 325 0.61% 146 0.52% 1,181 0.7%
Topic 14 1,596 2.97% 461 1.65% 2,885 1.7%
Topic 15 1,386 2.58% 642 2.3% 4,686 2.77%
Topic 16 1,407 2.62% 525 1.88% 3,651 2.16%
Topic 17 3,690 6.87% 1,459 5.23% 19,358 11.43%
Topic 18 1,026 1.91% 847 3.04% 4,760 2.81%
Topic 19 640 1.19% 423 1.52% 2,130 1.26%
Topic 20 872 1.62% 216 0.77% 2,262 1.34%
Topic 21 658 1.23% 363 1.3% 914 0.54%
Topic 22 818 1.52% 439 1.57% 1,965 1.16%
Topic 23 795 1.48% 518 1.86% 3,553 2.1%
Topic 24 385 0.72% 199 0.71% 1,079 0.64%
Topic 25 240 0.45% 74 0.27% 422 0.25%
Topic 26 788 1.47% 200 0.72% 1,738 1.03%
Topic 27 266 0.5% 120 0.43% 1,010 0.6%
Topic 28 847 1.58% 350 1.25% 3,135 1.85%
Topic 29 1,110 2.07% 327 1.17% 944 0.56%
Topic 30 1,132 2.11% 462 1.66% 6,444 3.81%
Topic 31 996 1.85% 975 3.49% 6,078 3.59%
Topic 32 76 0.14% 64 0.23% 335 0.2%
Topic 33 1,238 2.31% 985 3.53% 6,613 3.9%
Topic 34 1,124 2.09% 521 1.87% 3,335 1.97%
Topic 35 650 1.21% 657 2.35% 2,294 1.35%
Topic 36 601 1.12% 154 0.55% 548 0.32%
Topic 37 455 0.85% 194 0.7% 1,554 0.92%
Topic 38 1,246 2.32% 991 3.55% 2,849 1.68%
Topic 39 1,917 3.57% 936 3.35% 7,664 4.53%
Topic 40 848 1.58% 290 1.04% 2,419 1.43%
Topic 41 63 0.12% 40 0.14% 204 0.12%
Topic 42 853 1.59% 590 2.11% 2,016 1.19%
Topic 43 1,344 2.5% 604 2.16% 2,266 1.34%
Topic 44 814 1.52% 288 1.03% 3,005 1.77%
Topic 45 602 1.12% 474 1.7% 1,086 0.64%
Topic 46 709 1.32% 150 0.54% 1,646 0.97%
Topic 47 664 1.24% 245 0.88% 2,992 1.77%
Topic 48 940 1.75% 901 3.23% 3,045 1.8%
Topic 49 835 1.55% 563 2.02% 2,537 1.5%
Topic 50 1,328 2.47% 1,219 4.37% 3,421 2.02%
Topic 51 1,076 2% 323 1.16% 2,453 1.45%
Topic 52 196 0.36% 85 0.3% 758 0.45%
Topic 53 590 1.1% 293 1.05% 746 0.44%
Topic 54 1,057 1.97% 824 2.95% 5,570 3.29%
Topic 55 302 0.56% 157 0.56% 868 0.51%
Topic 56 535 1% 398 1.43% 847 0.5%
Topic 57 656 1.22% 314 1.13% 1,990 1.18%
Topic 58 468 0.87% 182 0.65% 1,125 0.66%
Topic 59 426 0.79% 183 0.66% 700 0.41%
Topic 60 562 1.05% 297 1.06% 1,389 0.82%
Topic 61 86 0.16% 28 0.1% 174 0.1%
Topic 62 550 1.02% 343 1.23% 746 0.44%
Topic 63 690 1.28% 252 0.9% 1,726 1.02%
Topic 64 594 1.11% 244 0.87% 2,247 1.33%
Topic 65 662 1.23% 457 1.64% 907 0.54%
Topic 66 1,493 2.78% 527 1.89% 4,073 2.41%
Topic 67 737 1.37% 451 1.62% 3,237 1.91%
Topic 68 279 0.52% 145 0.52% 547 0.32%
Topic 69 1 0% NA NA% NA NA%
\label{k69-topic-pyramid-pllot}K69 Pyramid Chart

K69 Pyramid Chart

\label{k69-topic-bar-plot}K69 Bar Chart

K69 Bar Chart

1.10.1.1 Word Occurences

The table below shows the ten most common words in each topic, and the ten words with the highest FREX score, a measure that uses a harmonic mean of word exclusivity and topic coherence (Airoldi & Bischof, 2016).

Topic Number Top Ten Words Top Ten FREX
Topic 1 secretary, state, tell, ministers, given, today, department, can, confirm, said secretary, state, confirm, tell, ministers, state’s, minister’s, explain, please, discussions
Topic 2 safety, register, registration, indicated, registered, electoral, risk, risks, number, individual registration, indicated, hse, canvass, register, gurkhas, safety, dissent, hare, trustee
Topic 3 make, sure, statement, progress, difference, northern, ireland, towards, representations, responsibilities statement, make, sure, progress, ireland, representations, difference, northern, milton, departmental
Topic 4 debt, water, credit, charges, pay, loan, loans, people, financial, cost payday, loan, lenders, debts, loans, debt, charges, water, high-cost, creditors
Topic 5 house, committee, parliament, leader, select, motion, parliamentary, debate, scrutiny, business select, leader, house, motion, committee, backbench, scrutiny, committees, benchers, parliamentary
Topic 6 new, development, work, need, investment, strategy, must, programme, working, also development, strategy, develop, project, regional, projects, partnership, together, developed, build
Topic 7 road, petition, residents, car, vehicles, petitioners, dogs, roads, site, house petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling, declares, drivers, accidents
Topic 8 important, agree, welcome, country, making, particularly, thank, part, makes, good agree, welcome, important, absolutely, makes, making, friend’s, thank, particularly, giving
Topic 9 companies, market, company, competition, energy, consumers, prices, price, consumer, customers competition, companies, market, wholesale, suppliers, company, regulator, ofgem, supplier, consumers
Topic 10 women, men, equality, women’s, discrimination, rights, gender, equal, woman, marriage gender, bishops, transgender, women’s, women, abortion, same-sex, marriage, equality, gay
Topic 11 energy, climate, fuel, change, green, carbon, emissions, gas, environmental, industry renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide, kyoto, carbon, climate
Topic 12 office, post, offices, royal, service, closure, mail, services, network, christmas offices, mail, sub-post, post, sub-postmasters, closures, consignia, swindon, closure, office
Topic 13 mr, north, south, east, west, spoke, friends, birmingham, talked, central ealing, spoke, dorset, lothian, ayrshire, glasgow, chris, southwark, pontefract, birmingham
Topic 14 pension, scheme, benefit, pensions, benefits, pensioners, system, credit, allowance, income pension, esa, pensions, claimants, retirement, pip, pensioners, incapacity, dwp, means-testing
Topic 15 economy, jobs, economic, growth, unemployment, country, investment, chancellor, budget, crisis unemployment, recession, growth, economy, obr, deficit, inflation, economic, forecast, borrowing
Topic 16 schools, school, education, children, teachers, parents, pupils, educational, special, primary academies, pupil, grammar, schools, pupils, teachers, ofsted, school, teacher, sen
Topic 17 want, say, one, think, know, need, us, get, go, see think, say, things, want, something, saying, going, lot, really, go
Topic 18 review, report, commission, independent, process, recommendations, inquiry, also, system, standards recommendations, inquiry, panel, audit, independent, recommendation, reviews, fsa, complaints, review
Topic 19 business, businesses, small, financial, bank, banks, insurance, rates, industry, enterprise smes, medium-sized, businesses, bank, enterprises, enterprise, banking, rbs, business, rock
Topic 20 wales, industry, welsh, north-east, england, assembly, constituency, jobs, manufacturing, uk welsh, wales, steel, cardiff, north-east, assembly, visteon, newcastle, manufacturing, tyneside
Topic 21 care, services, social, mental, need, health, home, provision, service, older mental, care, social, elderly, older, advocacy, services, residential, palliative, discharges
Topic 22 pay, work, workers, employment, working, wage, minimum, employers, paid, national wage, workers, zero-hours, employees, paternity, employer, minimum, employers, employment, workplace
Topic 23 amendment, clause, amendments, new, 1, lords, section, 2, act, clauses amendment, nos, insert, subsection, clause, amendments, clauses, section, lords, schedule
Topic 24 report, last, since, said, received, published, year, following, official, end march, vol, official, january, july, november, published, december, june, october
Topic 25 made, clear, impact, decision, changes, recent, assessment, decisions, effect, proposed made, decision, assessment, clear, decisions, impact, implications, recent, changes, effect
Topic 26 funding, cuts, fund, cut, budget, grant, spending, bbc, review, flood flood, funding, bbc, formula, grant, flooding, floods, cumbria, lottery, grants
Topic 27 money, spent, extra, spend, liberal, cost, spending, value, opposition, tory money, spent, liberal, spend, democrats, tories, tory, lib, democrat, conservatives
Topic 28 constituency, great, community, proud, many, sport, one, also, world, new maiden, arts, football, museum, museums, sport, olympic, games, sports, heritage
Topic 29 families, child, poverty, children, parents, work, credit, working, family, living lone, poverty, childcare, families, low-income, child, nursery, four-year-olds, nurseries, joseph
Topic 30 party, conservative, vote, parliament, political, election, labour, parties, scottish, elected party, vote, voting, conservative, party’s, voters, election, voted, votes, politics
Topic 31 point, can, may, issue, take, however, whether, matter, understand, consider matter, point, understand, consider, certainly, accept, possible, issue, course, happy
Topic 32 member, said, lady, mentioned, raised, comments, speech, referred, points, remarks member, lady, comments, remarks, bromley, interesting, chislehurst, pointed, front-bench, mentioned
Topic 33 european, uk, eu, countries, united, union, europe, states, british, trade accession, enlargement, wto, lisbon, treaty, eu, doha, european, negotiations, brexit
Topic 34 education, skills, young, training, students, university, college, higher, science, apprenticeships ema, fe, students, apprenticeship, universities, qualifications, apprenticeships, graduates, vocational, courses
Topic 35 local, authorities, authority, planning, community, communities, councils, area, guidance, system authorities, local, authority, planning, councils, councillors, locally, guidance, localism, communities
Topic 36 disabled, carers, disability, support, disabilities, needs, caring, autism, learning, can carers, autism, autistic, disabled, disabilities, disability, dementia, carer, caring, deaf
Topic 37 environment, marine, fishing, sea, industry, natural, fish, countryside, rural, fisheries fishermen, cod, forestry, biodiversity, habitats, mmo, fishing, fish, cfp, fisheries
Topic 38 justice, court, violence, victims, cases, criminal, domestic, courts, prison, offence attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking, prosecutor, prisons, prosecution
Topic 39 international, foreign, rights, human, peace, un, conflict, world, aid, war israel, palestinian, israeli, gaza, sri, zimbabwe, iran, yemen, hamas, palestinians
Topic 40 day, family, never, told, families, life, happened, constituent, man, went man, died, son, story, stories, hillsborough, tragedy, daughter, husband, angry
Topic 41 proposals, future, forward, consultation, plans, meet, paper, current, discuss, bring proposals, consultation, paper, plans, forward, discuss, white, proposal, meet, implement
Topic 42 behaviour, crime, antisocial, alcohol, young, drugs, drug, problem, use, tackle antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking, fireworks, behaviour, graffiti
Topic 43 housing, homes, social, affordable, private, home, accommodation, rent, need, properties housing, tenants, rented, tenancies, homelessness, leasehold, landlords, rents, properties, leaseholders
Topic 44 question, order, mr, put, asked, answer, questions, ask, speaker, time question, answer, questions, speaker, asked, deputy, answers, order, apologise, read
Topic 45 research, cancer, treatment, medical, condition, screening, disease, can, patients, use embryos, prostate, cervical, hepatitis, cloning, transplant, embryo, fertilisation, embryonic, endometriosis
Topic 46 online, internet, farmers, animals, digital, animal, broadband, sites, tickets, technology cull, badgers, badger, fur, bovine, mink, culling, circuses, touts, snares
Topic 47 defence, forces, armed, plymouth, personnel, service, military, army, nuclear, royal mod, naval, hms, submarines, dockyard, veterans, armed, plymouth, covenant, personnel
Topic 48 information, home, security, data, immigration, control, orders, system, terrorism, appeal extradition, tpims, sia, warrant, detention, checks, tpim, terrorism, intercept, identity
Topic 49 police, officers, crime, policing, home, force, service, forces, officer, chief constable, constables, officers, policing, police, soca, ipcc, constabulary, pcsos, hmic
Topic 50 nhs, hospital, patients, health, services, hospitals, care, service, trust, trusts dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital, dental, trusts, patients
Topic 51 tax, budget, cut, chancellor, cuts, rate, income, vat, benefit, hit 50p, vat, millionaires, hit, tax, allowances, credits, richest, chancellor, ifs
Topic 52 years, now, two, time, first, three, past, one, months, ago years, three, months, ago, two, past, weeks, five, four, now
Topic 53 staff, doctors, emergency, medical, service, training, nurses, royal, junior, ambulance ambulance, junior, staffing, doctors, halifax, posts, nurses, fss, staff, cpr
Topic 54 bill, legislation, act, law, rights, provisions, powers, regulations, place, believe bill, legislation, bill’s, provisions, passage, regulations, legislative, draft, statute, definition
Topic 55 public, sector, private, organisations, service, voluntary, services, society, community, organisation public, voluntary, organisations, sector, private, co-operative, volunteering, volunteers, volunteer, co-operatives
Topic 56 health, national, inequalities, programme, suicide, disease, department, prevention, among, risk flu, hiv, pandemic, inequalities, infections, suicide, mortality, infection, mrsa, vaccine
Topic 57 council, london, areas, city, area, constituency, centre, rural, county, liverpool county, mayor, borough, cities, liverpool, city, regeneration, council’s, london, towns
Topic 58 advice, legal, cases, civil, hull, aid, case, compensation, claims, service hull, tribunal, legal, compensation, solicitors, advice, concentrix, servants, lawyers, tribunals
Topic 59 people, work, many, young, get, people’s, can, help, lives, job people, people’s, get, getting, work, young, jobcentre, lives, youth, find
Topic 60 tax, revenue, relief, duty, uk, avoidance, hmrc, charities, companies, taxation evasion, hmrc, gaar, avoidance, inland, stamp, revenue, relief, gift, dependencies
Topic 61 government, government’s, policy, labour, previous, scotland, scottish, commitment, policies, coalition government, previous, policy, government’s, scotland, coalition, scottish, labour, disappointing, administrations
Topic 62 trafficking, home, uk, asylum, refugees, immigration, country, human, migration, britain trafficking, slavery, trafficked, sierra, leone, slave, dubs, fgm, yarl’s, wilberforce
Topic 63 food, products, industry, smoking, advertising, tobacco, ban, product, standards, shops gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets, labelling, retailers, packaging
Topic 64 members, debate, many, issues, also, today, heard, opportunity, hope, issue members, debate, heard, speak, sides, issues, hear, opportunity, listened, pleased
Topic 65 children, child, parents, young, children’s, family, contact, vulnerable, adoption, abuse csa, adopters, adoption, child’s, cafcass, looked-after, children’s, children, safeguarding, barred
Topic 66 transport, rail, bus, services, line, travel, train, network, passengers, london rail, passengers, passenger, heathrow, hs2, freight, high-speed, crossrail, airlines, runway
Topic 67 year, million, number, increase, figures, increased, billion, 1, average, cost million, figures, figure, increased, increase, compared, year, total, fallen, estimates
Topic 68 support, ensure, can, help, aware, taking, take, provide, action, continue aware, ensure, support, taking, steps, continue, help, action, assure, encourage
Topic 69 deal, recently, new, can, lack, great, concern, done, move, given deal, recently, lack, elsewhere, concern, great, improved, offered, done, new

1.10.2 Short lists vs Non-Short lists - K30

As seen in the word lists above, there is relatively scattershot semantic coherence, although exclusivity is high, when using the 69 topic models suggested by Lee and Mimno’s (2014) algorithm. We therefore re-ran the analysis, using 30 topic models, which has resulted in increased semantic coherence, albeit with slightly lower exclusivity, as illustrated in Figure .

\label{k30-network}Fruchterman-Reingold plot of K30 Network

Fruchterman-Reingold plot of K30 Network

\label{k30-coherence}Coherence of K30 Topic Models

Coherence of K30 Topic Models

Count and Distribution of Topics – K30
Topic Number AWS Speeches Percent of AWS Speeches Non-AWS Speeches Percent of non-AWS Speeches Male MP Speeches Percent of Male MP Speeches
Topic 1 1,792 3.34% 1,229 4.4% 8,163 4.82%
Topic 2 2,476 4.61% 2,514 9.01% 11,393 6.73%
Topic 3 1,082 2.01% 632 2.27% 926 0.55%
Topic 4 1,303 2.43% 900 3.23% 3,364 1.99%
Topic 5 1,976 3.68% 1,371 4.91% 9,653 5.7%
Topic 6 1,720 3.2% 623 2.23% 4,562 2.69%
Topic 7 2,721 5.07% 758 2.72% 4,045 2.39%
Topic 8 879 1.64% 381 1.37% 2,193 1.29%
Topic 9 1,008 1.88% 743 2.66% 1,747 1.03%
Topic 10 1,351 2.52% 658 2.36% 6,235 3.68%
Topic 11 2,144 3.99% 1,552 5.56% 4,494 2.65%
Topic 12 2,507 4.67% 883 3.16% 10,394 6.14%
Topic 13 1,231 2.29% 825 2.96% 3,972 2.35%
Topic 14 984 1.83% 646 2.32% 1,570 0.93%
Topic 15 1,180 2.2% 1,410 5.05% 4,935 2.91%
Topic 16 2,175 4.05% 1,302 4.67% 7,547 4.46%
Topic 17 5,309 9.89% 2,357 8.45% 25,255 14.91%
Topic 18 2,362 4.4% 1,003 3.59% 6,230 3.68%
Topic 19 1,183 2.2% 445 1.59% 3,305 1.95%
Topic 20 1,334 2.48% 561 2.01% 2,075 1.23%
Topic 21 4,361 8.12% 1,556 5.58% 11,845 6.99%
Topic 22 977 1.82% 359 1.29% 2,258 1.33%
Topic 23 1,787 3.33% 890 3.19% 6,124 3.62%
Topic 24 813 1.51% 233 0.84% 2,132 1.26%
Topic 25 1,604 2.99% 1,104 3.96% 4,917 2.9%
Topic 26 1,237 2.3% 664 2.38% 1,105 0.65%
Topic 27 668 1.24% 325 1.16% 1,796 1.06%
Topic 28 3,218 5.99% 1,001 3.59% 8,906 5.26%
Topic 29 1,121 2.09% 304 1.09% 4,463 2.64%
Topic 30 1,202 2.24% 673 2.41% 3,746 2.21%
\label{k30-topic-pyramid-plot}K30 Pyramid Chart

K30 Pyramid Chart

\label{k30-topic-bar-plot}K30 Bar Chart

K30 Bar Chart

1.10.2.1 Word Occurences

Words in topic
Topic Number Top Ten Words Top Ten FREX
Topic 1 bill, amendment, clause, new, legislation, amendments, act, committee, provisions, 1 amendment, clause, amendments, clauses, nos, insert, subsection, provisions, bill, tabled
Topic 2 issues, public, information, also, report, review, process, work, need, important consultation, review, guidance, recommendations, information, considering, decisions, arrangements, framework, detailed
Topic 3 women, men, pay, equality, rights, women’s, discrimination, equal, work, woman women, equality, gender, equalities, bishops, discrimination, female, women’s, equal, men
Topic 4 police, crime, officers, behaviour, policing, home, antisocial, community, work, force policing, antisocial, constable, burglary, wardens, crime, constabulary, police, officers, pcsos
Topic 5 european, uk, countries, eu, union, trade, international, united, world, british treaty, enlargement, wto, lisbon, doha, eu, eu’s, mod, multilateral, accession
Topic 6 transport, london, rail, bus, road, services, line, travel, network, train rail, bus, passengers, fares, trains, buses, passenger, heathrow, congestion, hs2
Topic 7 people, work, benefit, pension, benefits, support, disabled, employment, carers, working disabled, jobcentre, incapacity, carers, pension, claimants, esa, dla, pensions, atos
Topic 8 immigration, safety, uk, asylum, enforcement, home, number, illegal, licensing, animals dogs, dog, id, visa, fur, mink, hse, sia, seekers, fireworks
Topic 9 health, research, cancer, treatment, medical, disease, can, smoking, patients, people cancer, diseases, vaccine, flu, embryos, infections, diabetes, palliative, prostate, cervical
Topic 10 government, labour, conservative, party, opposition, policy, government’s, scotland, scottish, members conservative, liberal, democrats, conservatives, scottish, democrat, scotland, tory, interruption, tories
Topic 11 care, health, nhs, services, service, hospital, patients, staff, trust, social dentists, ambulance, dentistry, helier, dentist, nurses, hospital, pct, hospitals, dental
Topic 12 member, members, debate, house, mr, committee, said, time, speaker, north member, speaker, mr, debate, spoke, thoughtful, backbench, debates, madam, select
Topic 13 companies, financial, company, market, scheme, money, debt, consumers, bank, credit payday, annuity, oft, policyholders, penrose, fca, loan, prepayment, loans, annuities
Topic 14 young, people, health, mental, youth, prison, problems, drugs, alcohol, drug prisons, probation, cannabis, reoffending, mental, prison, self-harm, youth, alcohol, sentences
Topic 15 cases, court, legal, law, case, justice, evidence, criminal, courts, home judicial, attorney-general, defendant, extradition, tpims, suspects, court, courts, prosecution, isc
Topic 16 energy, businesses, business, jobs, investment, economy, industry, economic, new, sector carbon, renewable, renewables, solar, low-carbon, energy, feed-in, manufacturing, steel, businesses
Topic 17 people, want, one, get, know, say, us, many, think, need things, think, something, get, want, going, really, say, lot, go
Topic 18 education, schools, school, children, training, skills, parents, teachers, students, young schools, teachers, pupils, curriculum, sen, academies, ofsted, pupil, grammar, attainment
Topic 19 constituency, city, people, many, years, work, centre, one, hull, great fishermen, cod, hull, plymouth, maiden, fishing, fish, humber, fleetwood, tourism
Topic 20 housing, homes, people, private, london, social, home, affordable, need, accommodation rent, tenants, landlords, rented, homelessness, homeless, rents, tenancies, housing, tenancy
Topic 21 tax, year, million, government, budget, cuts, cut, poverty, increase, billion tax, obr, vat, millionaires, 50p, inflation, budget, fiscal, chancellor, cut
Topic 22 food, post, office, rural, petition, offices, farmers, royal, mail, government petition, farmers, petitioners, meat, cull, labelling, cattle, badger, culling, beef
Topic 23 people, international, human, government, war, rights, country, un, conflict, world syria, israel, civilians, palestinian, israeli, gaza, sri, holocaust, hatred, sierra
Topic 24 bbc, media, online, internet, sport, access, digital, culture, clubs, football bbc, games, olympic, gambling, bbc’s, copyright, lap-dancing, broadband, radio, internet
Topic 25 local, authorities, funding, areas, services, council, community, authority, government, communities local, authorities, funding, councils, grant, authority, formula, deprived, areas, partnership
Topic 26 children, child, families, care, family, parents, violence, support, domestic, victims trafficked, csa, same-sex, adopters, child, rape, marriages, marriage, sexual, couples
Topic 27 planning, water, development, land, environment, site, sites, flood, environmental, area forestry, biodiversity, masts, habitats, gypsy, flood, waterways, flooding, marine, mmo
Topic 28 secretary, state, house, last, statement, report, said, now, question, answer secretary, statement, state, confirm, official, answer, vol, state’s, letter, written
Topic 29 parliament, wales, vote, commission, political, assembly, people, welsh, elected, charities electoral, polling, gibraltar, voting, assembly, vote, votes, voter, ballot, elections
Topic 30 can, make, ensure, agree, important, take, made, point, sure, welcome agree, aware, sure, ensure, taking, lady, welcome, steps, point, make

1.11 Discussion

There do not appear to be substantial or meaningful differences in the speaking styles of female Labour MPs selected through all women short lists when compared to their female colleagues selected through open short lists using LIWC. This is possibly due to the speaking style dominant in British parliamentary debate, which is more formal than the speech used in most day-to-day conversation. LIWC has American developers, and the dictionary may not be able to capture stylistic differences between American and British English, and may not include words commonly used in formal British English speech, limiting its usefulness in a British context.

There is more gender distinction in some selected terms and topics. AWS MPs are far more likely to make reference to their constituency and their constituents. In the debate between whether MPs should be “delegates” or “trustees” – the “mandate-independence controversy” outlined by Pitkin (1967) – the references to their constituents and constituencies suggests AWS MPs shy away from the Burkean concept of trusteeship and see themselves more as strict representatives of their constituents. In Andeweg & Thomassen’s (2005) typology of ex ante/ex post and above/below political representation, AWS MPs lean towards representation “from below”, although their selection process is ex ante/ex post.

AWS MPs refer to their constituents both specifically and in the abstract, particularly when criticising government policy. For example, in debate on 4th March 2015, Gemma Doyle, than the Labour MP for West Dunbartonshire (elected on an AWS in 2010), when asked if she would give way to Conservative MP Stephen Mosley, responded:

No, I will not [give way], because my constituents want me to make these points, not to give more time to Conservative Members.

On 2nd June 2010, during debate on Israel-Palestine, Valerie Vaz, MP for Walsall South:

My constituents want more than pressure. Will the Foreign Secretary come back to the House and report on a timetable for the discussions on a diplomatic solution, just as we did on Ireland?

On 4th April 2001, Betty Williams, member for Conwy from 1997–2010, raised the case of a wilderness guide in her constituency unable to access parts of the countryside due to foot and mouth disease:

Is my right hon. Friend aware that there is continuing concern about the limited access to the countryside and crags of north Wales? May I draw his attention to the circumstances of my constituent, Ric Potter? Like many others, he has had to travel to Scotland, where there is greater access. Will my right hon. Friend help us to enable people such as Ric Potter to find work in outdoor pursuits?

2 Appendix

2.1 Full topic model summary - K69

## A topic model with 69 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
##       Highest Prob: secretary, state, tell, ministers, given, today, department 
##       FREX: secretary, state, confirm, tell, ministers, state's, minister's 
##       Lift: dhar, lowell, qatada's, #nationalistsconfused, 1135, 2.5bn, 36,500 
##       Score: secretary, state, confirm, state's, tell, ministers, department 
## Topic 2 Top Words:
##       Highest Prob: safety, register, registration, indicated, registered, electoral, risk 
##       FREX: registration, indicated, hse, canvass, register, gurkhas, safety 
##       Lift: aps, hse, 10-litre, 13a, 14,940, 1760, 1867 
##       Score: safety, registration, register, electoral, indicated, registered, hse 
## Topic 3 Top Words:
##       Highest Prob: make, sure, statement, progress, difference, northern, ireland 
##       FREX: statement, make, sure, progress, ireland, representations, difference 
##       Lift: 101269, 101548, 102938, 103414, 106583, 107305, 109413 
##       Score: make, statement, progress, sure, ireland, northern, milton 
## Topic 4 Top Words:
##       Highest Prob: debt, water, credit, charges, pay, loan, loans 
##       FREX: payday, loan, lenders, debts, loans, debt, charges 
##       Lift: 1,021, 1,025, 1,106, 1,189, 1,273, 1,385, 1,413 
##       Score: debt, water, payday, loan, loans, lenders, credit 
## Topic 5 Top Words:
##       Highest Prob: house, committee, parliament, leader, select, motion, parliamentary 
##       FREX: select, leader, house, motion, committee, backbench, scrutiny 
##       Lift: e-petitions, praying-against, sherlock, wednesdays, sittings, thursdays, 10,000-signature 
##       Score: committee, house, leader, select, scrutiny, parliament, motion 
## Topic 6 Top Words:
##       Highest Prob: new, development, work, need, investment, strategy, must 
##       FREX: development, strategy, develop, project, regional, projects, partnership 
##       Lift: #1,150, 1.245, 1.875, 128406, 131,000, 18-the, 2002-around 
##       Score: development, regional, investment, strategy, infrastructure, projects, work 
## Topic 7 Top Words:
##       Highest Prob: road, petition, residents, car, vehicles, petitioners, dogs 
##       FREX: petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling 
##       Lift: 0.037, 0.044, 0fficial, 1,042, 1,072, 1,108, 1,122 
##       Score: petitioners, petition, dogs, road, residents, dog, declares 
## Topic 8 Top Words:
##       Highest Prob: important, agree, welcome, country, making, particularly, thank 
##       FREX: agree, welcome, important, absolutely, makes, making, friend's 
##       Lift: adequacies, and-importantly-much, ayse, ballantine, bobtail-and, bostrom, bucketfuls 
##       Score: agree, important, thank, welcome, friend's, absolutely, country 
## Topic 9 Top Words:
##       Highest Prob: companies, market, company, competition, energy, consumers, prices 
##       FREX: competition, companies, market, wholesale, suppliers, company, regulator 
##       Lift: 1,105, 1,345, ashington, boakye, nord, over-charging, price-fixing 
##       Score: companies, consumers, energy, market, company, prices, competition 
## Topic 10 Top Words:
##       Highest Prob: women, men, equality, women's, discrimination, rights, gender 
##       FREX: gender, bishops, transgender, women's, women, abortion, same-sex 
##       Lift: balmforth, bpas, celebrants, cohabitants, jessy, msafiri, natal 
##       Score: women, women's, equality, men, gender, discrimination, marriage 
## Topic 11 Top Words:
##       Highest Prob: energy, climate, fuel, change, green, carbon, emissions 
##       FREX: renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide 
##       Lift: fossil, #2,500, #solar, 1-are, 1,129, 1,214, 1,343 
##       Score: energy, fuel, carbon, emissions, climate, renewable, renewables 
## Topic 12 Top Words:
##       Highest Prob: office, post, offices, royal, service, closure, mail 
##       FREX: offices, mail, sub-post, post, sub-postmasters, closures, consignia 
##       Lift: #1.8, #210, #450, 1,001, 1,352, 1,639, 1,827 
##       Score: post, offices, office, mail, closure, postal, sub-post 
## Topic 13 Top Words:
##       Highest Prob: mr, north, south, east, west, spoke, friends 
##       FREX: ealing, spoke, dorset, lothian, ayrshire, glasgow, chris 
##       Lift: argos's, blackford, cairns's, clemency, marxist, no2id, 0.66 
##       Score: mr, east, north, south, west, spoke, birmingham 
## Topic 14 Top Words:
##       Highest Prob: pension, scheme, benefit, pensions, benefits, pensioners, system 
##       FREX: pension, esa, pensions, claimants, retirement, pip, pensioners 
##       Lift: means-testing, #20,000, #400, 0º, 1,052, 1,366, 1,482 
##       Score: pension, pensions, pensioners, allowance, scheme, retirement, credit 
## Topic 15 Top Words:
##       Highest Prob: economy, jobs, economic, growth, unemployment, country, investment 
##       FREX: unemployment, recession, growth, economy, obr, deficit, inflation 
##       Lift: 0.76, 0.83, 1,196, 1,319, 1.04, 1.32, 10-about 
##       Score: economy, jobs, unemployment, growth, economic, recession, chancellor 
## Topic 16 Top Words:
##       Highest Prob: schools, school, education, children, teachers, parents, pupils 
##       FREX: academies, pupil, grammar, schools, pupils, teachers, ofsted 
##       Lift: sen2, 11-plus, leas, pupil, 000-to, 1-regardless, 1,000-pupil 
##       Score: schools, school, teachers, pupils, children, education, parents 
## Topic 17 Top Words:
##       Highest Prob: want, say, one, think, know, need, us 
##       FREX: think, say, things, want, something, saying, going 
##       Lift: about-part, arguing-i, beneficial-we, career-many, cause-the, clam, compatriot 
##       Score: think, want, get, say, things, going, us 
## Topic 18 Top Words:
##       Highest Prob: review, report, commission, independent, process, recommendations, inquiry 
##       FREX: recommendations, inquiry, panel, audit, independent, recommendation, reviews 
##       Lift: bourn, cag's, clarke's, clowes, dg, emag, equitable's 
##       Score: fsa, inquiry, review, commission, recommendations, report, independent 
## Topic 19 Top Words:
##       Highest Prob: business, businesses, small, financial, bank, banks, insurance 
##       FREX: smes, medium-sized, businesses, bank, enterprises, enterprise, banking 
##       Lift: 0.21, 0.84, 1,034, 1,130, 10-fold, 1130, 12.19 
##       Score: businesses, business, bank, banks, banking, insurance, small 
## Topic 20 Top Words:
##       Highest Prob: wales, industry, welsh, north-east, england, assembly, constituency 
##       FREX: welsh, wales, steel, cardiff, north-east, assembly, visteon 
##       Lift: a55, angharad, bethesda, co-investment, dewhirst, dogger, gorge 
##       Score: wales, welsh, assembly, manufacturing, steel, north-east, yorkshire 
## Topic 21 Top Words:
##       Highest Prob: care, services, social, mental, need, health, home 
##       FREX: mental, care, social, elderly, older, advocacy, services 
##       Lift: #900,000, 1,051, 1,312, 10,758, 104962, 1089, 13,198 
##       Score: care, mental, services, social, health, older, homes 
## Topic 22 Top Words:
##       Highest Prob: pay, work, workers, employment, working, wage, minimum 
##       FREX: wage, workers, zero-hours, employees, paternity, employer, minimum 
##       Lift: 6.70, 8.20, 85p, awb, e-balloting, hannett, increments 
##       Score: wage, workers, employers, employment, pay, employees, minimum 
## Topic 23 Top Words:
##       Highest Prob: amendment, clause, amendments, new, 1, lords, section 
##       FREX: amendment, nos, insert, subsection, clause, amendments, clauses 
##       Lift: 153a, 22a, 287, 50b, 51b, counter-notice, insured's 
##       Score: clause, amendment, amendments, lords, nos, insert, subsection 
## Topic 24 Top Words:
##       Highest Prob: report, last, since, said, received, published, year 
##       FREX: march, vol, official, january, july, november, published 
##       Lift: 1-2ws, 1,033, 1,099, 1,124,818, 1,337, 1,368,186, 1,595 
##       Score: report, official, vol, published, march, april, november 
## Topic 25 Top Words:
##       Highest Prob: made, clear, impact, decision, changes, recent, assessment 
##       FREX: made, decision, assessment, clear, decisions, impact, implications 
##       Lift: 104963, 107312, 116,400, 125214, 125828, 125830, 126370 
##       Score: made, assessment, impact, changes, decision, decisions, clear 
## Topic 26 Top Words:
##       Highest Prob: funding, cuts, fund, cut, budget, grant, spending 
##       FREX: flood, funding, bbc, formula, grant, flooding, floods 
##       Lift: #10.89, #12, #3.3, #bbcdiversity, 1,027, 1,536-will, 1,546 
##       Score: funding, cuts, flood, bbc, budget, spending, flooding 
## Topic 27 Top Words:
##       Highest Prob: money, spent, extra, spend, liberal, cost, spending 
##       FREX: money, spent, liberal, spend, democrats, tories, tory 
##       Lift: 1-but, 10,309.63, 1228, 158.8, 1763, 18-that, 1979-80 
##       Score: money, liberal, tory, democrats, conservatives, tories, spending 
## Topic 28 Top Words:
##       Highest Prob: constituency, great, community, proud, many, sport, one 
##       FREX: maiden, arts, football, museum, museums, sport, olympic 
##       Lift: 0.27, 0.51, 1,084, 1,126, 1,468, 1,580-plus, 1,983 
##       Score: arts, sport, museum, maiden, heritage, football, constituency 
## Topic 29 Top Words:
##       Highest Prob: families, child, poverty, children, parents, work, credit 
##       FREX: lone, poverty, childcare, families, low-income, child, nursery 
##       Lift: 1,000-discriminates, 1,000-not, 1,080, 1,142,600, 1,170, 1,390, 1,664 
##       Score: poverty, child, families, children, parents, credit, lone 
## Topic 30 Top Words:
##       Highest Prob: party, conservative, vote, parliament, political, election, labour 
##       FREX: party, vote, voting, conservative, party's, voters, election 
##       Lift: alphabetical, gentry, olga, one-party, randomisation, 1,166, 1,294 
##       Score: party, conservative, vote, scottish, election, elections, political 
## Topic 31 Top Words:
##       Highest Prob: point, can, may, issue, take, however, whether 
##       FREX: matter, point, understand, consider, certainly, accept, possible 
##       Lift: 450,000-in, advised-by, backslid, bill-albeit, bizarre-but, can-enable, cases-roughly 
##       Score: point, matter, issue, gentleman's, consider, shall, whether 
## Topic 32 Top Words:
##       Highest Prob: member, said, lady, mentioned, raised, comments, speech 
##       FREX: member, lady, comments, remarks, bromley, interesting, chislehurst 
##       Lift: 12.20, 130b, 1991-forcing, 2,784, 54,000-worth, achieved-is, arguments-and 
##       Score: member, lady, comments, said, speech, raised, points 
## Topic 33 Top Words:
##       Highest Prob: european, uk, eu, countries, united, union, europe 
##       FREX: accession, enlargement, wto, lisbon, treaty, eu, doha 
##       Lift: 13652, 13653, 13654, 1707, balkan, barnier, blackmailing 
##       Score: eu, european, countries, union, treaty, europe, trade 
## Topic 34 Top Words:
##       Highest Prob: education, skills, young, training, students, university, college 
##       FREX: ema, fe, students, apprenticeship, universities, qualifications, apprenticeships 
##       Lift: ema, #ne, 1,188, 1,308, 1,555-what, 1,740, 1,803 
##       Score: students, education, young, skills, apprenticeships, training, universities 
## Topic 35 Top Words:
##       Highest Prob: local, authorities, authority, planning, community, communities, councils 
##       FREX: authorities, local, authority, planning, councils, councillors, locally 
##       Lift: achcew, central-local, laa, lsp, maas, observances, place-shaping 
##       Score: local, authorities, authority, councils, planning, communities, community 
## Topic 36 Top Words:
##       Highest Prob: disabled, carers, disability, support, disabilities, needs, caring 
##       FREX: carers, autism, autistic, disabled, disabilities, disability, dementia 
##       Lift: autism, rnib, #185, #85, #hellomynameis, 1,400-one, 10-person 
##       Score: carers, disabled, disability, autism, disabilities, caring, dementia 
## Topic 37 Top Words:
##       Highest Prob: environment, marine, fishing, sea, industry, natural, fish 
##       FREX: fishermen, cod, forestry, biodiversity, habitats, mmo, fishing 
##       Lift: aquaculture, arable, bee-friendly, biodiversity, birdlife, bycatch, caterpillar 
##       Score: marine, fishing, fishermen, fish, fisheries, wildlife, conservation 
## Topic 38 Top Words:
##       Highest Prob: justice, court, violence, victims, cases, criminal, domestic 
##       FREX: attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking 
##       Lift: #9, 0.08, 0.48, 1,046, 1,237, 10,544, 10.15 
##       Score: violence, prison, court, offence, criminal, rape, victims 
## Topic 39 Top Words:
##       Highest Prob: international, foreign, rights, human, peace, un, conflict 
##       FREX: israel, palestinian, israeli, gaza, sri, zimbabwe, iran 
##       Lift: lankan, saddam, #aleppo, 1,010, 1,476, 1,591, 1224 
##       Score: un, israel, syria, humanitarian, palestinian, israeli, iraq 
## Topic 40 Top Words:
##       Highest Prob: day, family, never, told, families, life, happened 
##       FREX: man, died, son, story, stories, hillsborough, tragedy 
##       Lift: 10,000-seat, 12-inch, 1234, 1519-20, 1635, 1710, 174,995 
##       Score: families, holocaust, family, constituent, man, died, mother 
## Topic 41 Top Words:
##       Highest Prob: proposals, future, forward, consultation, plans, meet, paper 
##       FREX: proposals, consultation, paper, plans, forward, discuss, white 
##       Lift: 10.42, 107910, 109648, 114061, 119621, 141605, 141607 
##       Score: proposals, consultation, plans, future, forward, paper, white 
## Topic 42 Top Words:
##       Highest Prob: behaviour, crime, antisocial, alcohol, young, drugs, drug 
##       FREX: antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking 
##       Lift: acquisitive, addaction, auto, bailes, crawlers, ghb, gilpin 
##       Score: antisocial, crime, behaviour, alcohol, drug, drugs, cannabis 
## Topic 43 Top Words:
##       Highest Prob: housing, homes, social, affordable, private, home, accommodation 
##       FREX: housing, tenants, rented, tenancies, homelessness, leasehold, landlords 
##       Lift: one-for-one, one-bedroom, rented, right-to-buy, #19, #21.5, #28.5 
##       Score: housing, homes, tenants, rented, rent, landlords, affordable 
## Topic 44 Top Words:
##       Highest Prob: question, order, mr, put, asked, answer, questions 
##       FREX: question, answer, questions, speaker, asked, deputy, answers 
##       Lift: 11.00, 11.57, 12.26, 1223, 1232, 1412, 1555-56 
##       Score: question, speaker, mr, answer, deputy, order, questions 
## Topic 45 Top Words:
##       Highest Prob: research, cancer, treatment, medical, condition, screening, disease 
##       FREX: embryos, prostate, cervical, hepatitis, cloning, transplant, embryo 
##       Lift: abnormalities, cystic, embryo, fertilisation, marrow, @cfaware, #500 
##       Score: cancer, patients, embryos, screening, treatment, tissue, breast 
## Topic 46 Top Words:
##       Highest Prob: online, internet, farmers, animals, digital, animal, broadband 
##       FREX: cull, badgers, badger, fur, bovine, mink, culling 
##       Lift: culling, @daisydumble, @donna_smiley, @jimspin, @leamingtonsbc, @maggieannehayes, @nhconsortium 
##       Score: farmers, animals, internet, cull, animal, online, badgers 
## Topic 47 Top Words:
##       Highest Prob: defence, forces, armed, plymouth, personnel, service, military 
##       FREX: mod, naval, hms, submarines, dockyard, veterans, armed 
##       Lift: hms, submarine, submarines, 1,000-people, 1,625, 1,705, 10-3 
##       Score: defence, armed, forces, plymouth, military, personnel, mod 
## Topic 48 Top Words:
##       Highest Prob: information, home, security, data, immigration, control, orders 
##       FREX: extradition, tpims, sia, warrant, detention, checks, tpim 
##       Lift: carlile's, carlile, sia, 10-month, 10,410, 10,500-for, 10.45 
##       Score: immigration, terrorism, detention, terrorist, tpims, home, security 
## Topic 49 Top Words:
##       Highest Prob: police, officers, crime, policing, home, force, service 
##       FREX: constable, constables, officers, policing, police, soca, ipcc 
##       Lift: 2003-morecambe, 9,650, a19s, ashleys, bigg, bounties, ckp 
##       Score: police, officers, policing, crime, forces, constable, neighbourhood 
## Topic 50 Top Words:
##       Highest Prob: nhs, hospital, patients, health, services, hospitals, care 
##       FREX: dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital 
##       Lift: acos, bequest, bernstein, bodmin, catto, cayton, chailey 
##       Score: nhs, patients, hospital, health, patient, hospitals, care 
## Topic 51 Top Words:
##       Highest Prob: tax, budget, cut, chancellor, cuts, rate, income 
##       FREX: 50p, vat, millionaires, hit, tax, allowances, credits 
##       Lift: #840, 0.76p, 1,003, 1,009, 1,226, 1,275, 1,296 
##       Score: tax, vat, budget, credits, chancellor, cuts, income 
## Topic 52 Top Words:
##       Highest Prob: years, now, two, time, first, three, past 
##       FREX: years, three, months, ago, two, past, weeks 
##       Lift: 10-week-old, 10,616, 11-month, 11-point, 1758, 196b, 63,500 
##       Score: years, months, two, ago, three, past, weeks 
## Topic 53 Top Words:
##       Highest Prob: staff, doctors, emergency, medical, service, training, nurses 
##       FREX: ambulance, junior, staffing, doctors, halifax, posts, nurses 
##       Lift: #i'm, 03, 1-who, 1,454, 1,631, 10,000-strong, 10,000-with 
##       Score: staff, doctors, ambulance, nurses, medical, emergency, junior 
## Topic 54 Top Words:
##       Highest Prob: bill, legislation, act, law, rights, provisions, powers 
##       FREX: bill, legislation, bill's, provisions, passage, regulations, legislative 
##       Lift: 1865, 1990s-when, 1998-it, 19may2000, 2003-largely, 2005-have, 29-year 
##       Score: bill, legislation, provisions, rights, law, powers, regulations 
## Topic 55 Top Words:
##       Highest Prob: public, sector, private, organisations, service, voluntary, services 
##       FREX: public, voluntary, organisations, sector, private, co-operative, volunteering 
##       Lift: 1844, af, carpetbaggers, nebulous, puk, 1075, 170-year 
##       Score: public, sector, private, voluntary, organisations, service, services 
## Topic 56 Top Words:
##       Highest Prob: health, national, inequalities, programme, suicide, disease, department 
##       FREX: flu, hiv, pandemic, inequalities, infections, suicide, mortality 
##       Lift: kirkley, lowestoft, nihr, acupuncture, influenza, #148, #3.6 
##       Score: health, vaccine, flu, inequalities, hiv, infection, suicide 
## Topic 57 Top Words:
##       Highest Prob: council, london, areas, city, area, constituency, centre 
##       FREX: county, mayor, borough, cities, liverpool, city, regeneration 
##       Lift: #12,000, #14.4, #356, #38, #5,000, #500,000, #66.6 
##       Score: london, council, city, regeneration, county, rural, borough 
## Topic 58 Top Words:
##       Highest Prob: advice, legal, cases, civil, hull, aid, case 
##       FREX: hull, tribunal, legal, compensation, solicitors, advice, concentrix 
##       Lift: 0300, 1,997, 1.148, 1.4m, 112.8, 1147, 128,687 
##       Score: legal, advice, hull, aid, compensation, civil, tribunal 
## Topic 59 Top Words:
##       Highest Prob: people, work, many, young, get, people's, can 
##       FREX: people, people's, get, getting, work, young, jobcentre 
##       Lift: 2,425, 294,488, 3,699, 37,290, 5,320, 50-to-64, 75589 
##       Score: people, young, work, get, youth, many, people's 
## Topic 60 Top Words:
##       Highest Prob: tax, revenue, relief, duty, uk, avoidance, hmrc 
##       FREX: evasion, hmrc, gaar, avoidance, inland, stamp, revenue 
##       Lift: 1,643, 3.12, 32.2, 44a, 80g, aaronson, aat 
##       Score: tax, hmrc, avoidance, revenue, relief, evasion, territories 
## Topic 61 Top Words:
##       Highest Prob: government, government's, policy, labour, previous, scotland, scottish 
##       FREX: government, previous, policy, government's, scotland, coalition, scottish 
##       Lift: 2005-perhaps, 80994, actually-that, agency-when, agenda-access, alloway, aware-in 
##       Score: government, scotland, scottish, labour, policy, government's, previous 
## Topic 62 Top Words:
##       Highest Prob: trafficking, home, uk, asylum, refugees, immigration, country 
##       FREX: trafficking, slavery, trafficked, sierra, leone, slave, dubs 
##       Lift: #7, 0.025, 1-yes, 1,060, 1,483, 1,746, 1.123 
##       Score: trafficking, refugees, asylum, slavery, trafficked, immigration, sierra 
## Topic 63 Top Words:
##       Highest Prob: food, products, industry, smoking, advertising, tobacco, ban 
##       FREX: gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets 
##       Lift: 0.7p, 00, 0157, 1,000-almost, 1,032, 1,200-i, 1,666 
##       Score: food, smoking, products, tobacco, advertising, gambling, industry 
## Topic 64 Top Words:
##       Highest Prob: members, debate, many, issues, also, today, heard 
##       FREX: members, debate, heard, speak, sides, issues, hear 
##       Lift: noakes, 6.47pm, accept-or, analysis-but, aspect-listening-is, bunfight, called-making 
##       Score: members, debate, issues, many, opposition, heard, constituents 
## Topic 65 Top Words:
##       Highest Prob: children, child, parents, young, children's, family, contact 
##       FREX: csa, adopters, adoption, child's, cafcass, looked-after, children's 
##       Lift: csa, @mandatenow, 10-month-old, 10-went, 12j, 150765, 16-only 
##       Score: children, child, parents, young, children's, adoption, child's 
## Topic 66 Top Words:
##       Highest Prob: transport, rail, bus, services, line, travel, train 
##       FREX: rail, passengers, passenger, heathrow, hs2, freight, high-speed 
##       Lift: 12-car, 15.15, 50.1, adtranz, anti-icing, bahn, chilterns 
##       Score: rail, transport, bus, passengers, fares, trains, hs2 
## Topic 67 Top Words:
##       Highest Prob: year, million, number, increase, figures, increased, billion 
##       FREX: million, figures, figure, increased, increase, compared, year 
##       Lift: #112, #3,850, #84.3, #87.2, 1,249, 102269, 122.9 
##       Score: million, year, billion, increase, figures, average, increased 
## Topic 68 Top Words:
##       Highest Prob: support, ensure, can, help, aware, taking, take 
##       FREX: aware, ensure, support, taking, steps, continue, help 
##       Lift: 103684, 103965, 107320, 111532, 112584, 113698, 117890 
##       Score: support, ensure, steps, aware, help, taking, department 
## Topic 69 Top Words:
##       Highest Prob: deal, recently, new, can, lack, great, concern 
##       FREX: deal, recently, lack, elsewhere, concern, great, improved 
##       Lift: 2004-a, 721,000, added-gva-per, age-of, centres-jacs-which, employment-can, index-the 
##       Score: deal, recently, new, worktrack, lack, can, great

2.2 Full topic model estimate summary - K69

## 
## Call:
## estimateEffect(formula = 1:69 ~ short_list, stmobj = topic_model2, 
##     metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0170490  0.0002872   59.37 <0.0000000000000002 ***
## short_listTRUE 0.0068967  0.0003515   19.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 2:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0058952  0.0002397  24.596 <0.0000000000000002 ***
## short_listTRUE 0.0007065  0.0003026   2.335              0.0196 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 3:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0087151  0.0001117  78.033 <0.0000000000000002 ***
## short_listTRUE 0.0002122  0.0001471   1.442               0.149    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 4:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0064766  0.0003078  21.041 <0.0000000000000002 ***
## short_listTRUE 0.0036170  0.0003905   9.262 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 5:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0120578  0.0002548   47.32 <0.0000000000000002 ***
## short_listTRUE 0.0035458  0.0003329   10.65 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 6:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0313826  0.0004269   73.50 <0.0000000000000002 ***
## short_listTRUE -0.0093819  0.0004914  -19.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 7:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0082027  0.0003883  21.126 < 0.0000000000000002 ***
## short_listTRUE 0.0024224  0.0004804   5.043           0.00000046 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 8:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0155991  0.0001353 115.293 < 0.0000000000000002 ***
## short_listTRUE -0.0006691  0.0001476  -4.532           0.00000585 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 9:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0106917  0.0002678  39.930 < 0.0000000000000002 ***
## short_listTRUE -0.0025223  0.0003462  -7.285    0.000000000000325 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 10:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0147928  0.0004467   33.12 <0.0000000000000002 ***
## short_listTRUE 0.0002684  0.0005480    0.49               0.624    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 11:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0131074  0.0004309  30.419 <0.0000000000000002 ***
## short_listTRUE -0.0008988  0.0005145  -1.747              0.0806 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 12:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0100377  0.0003281  30.597 < 0.0000000000000002 ***
## short_listTRUE -0.0026488  0.0003772  -7.023     0.00000000000219 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 13:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0084694  0.0002244  37.745 < 0.0000000000000002 ***
## short_listTRUE 0.0015081  0.0002907   5.189          0.000000212 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 14:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0133791  0.0004686   28.55 <0.0000000000000002 ***
## short_listTRUE 0.0060922  0.0005583   10.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 15:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0169596  0.0004287  39.556 < 0.0000000000000002 ***
## short_listTRUE 0.0029699  0.0005137   5.782        0.00000000742 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 16:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0135196  0.0005197  26.017 < 0.0000000000000002 ***
## short_listTRUE 0.0032504  0.0006708   4.846           0.00000126 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 17:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0425088  0.0003149 134.990 <0.0000000000000002 ***
## short_listTRUE 0.0008102  0.0003849   2.105              0.0353 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 18:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0268601  0.0004987   53.86 <0.0000000000000002 ***
## short_listTRUE -0.0068300  0.0005996  -11.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 19:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0129044  0.0003253  39.673 < 0.0000000000000002 ***
## short_listTRUE -0.0016916  0.0003815  -4.434           0.00000925 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 20:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0086108  0.0003453   24.94 <0.0000000000000002 ***
## short_listTRUE 0.0060656  0.0004412   13.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 21:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0124577  0.0003104   40.14 < 0.0000000000000002 ***
## short_listTRUE -0.0011221  0.0003965   -2.83              0.00465 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 22:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0127304  0.0003168   40.18 <0.0000000000000002 ***
## short_listTRUE 0.0008321  0.0004019    2.07              0.0384 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 23:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0185865  0.0005606  33.153 < 0.0000000000000002 ***
## short_listTRUE -0.0028071  0.0006255  -4.488            0.0000072 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 24:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0138634  0.0002294   60.42 < 0.0000000000000002 ***
## short_listTRUE 0.0013380  0.0002841    4.71           0.00000248 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 25:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0111247  0.0001346  82.629 < 0.0000000000000002 ***
## short_listTRUE 0.0007898  0.0001681   4.697           0.00000265 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 26:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0098005  0.0002865  34.206 <0.0000000000000002 ***
## short_listTRUE 0.0037734  0.0003825   9.864 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 27:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0103148  0.0002170   47.54 < 0.0000000000000002 ***
## short_listTRUE 0.0009108  0.0002727    3.34             0.000839 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 28:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0111687  0.0004059  27.517 < 0.0000000000000002 ***
## short_listTRUE 0.0027939  0.0005148   5.428         0.0000000573 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 29:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0105119  0.0003580  29.363 <0.0000000000000002 ***
## short_listTRUE 0.0042201  0.0004476   9.428 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 30:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0169375  0.0004454  38.029 <0.0000000000000002 ***
## short_listTRUE 0.0008002  0.0005608   1.427               0.154    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 31:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0369135  0.0002231  165.46 <0.0000000000000002 ***
## short_listTRUE -0.0068067  0.0002839  -23.98 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 32:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0100393  0.0001334  75.244 <0.0000000000000002 ***
## short_listTRUE -0.0002580  0.0001609  -1.603               0.109    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 33:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0230342  0.0005126   44.94 <0.0000000000000002 ***
## short_listTRUE -0.0059588  0.0006435   -9.26 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 34:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0136733  0.0004434  30.837 <0.0000000000000002 ***
## short_listTRUE 0.0011096  0.0005518   2.011              0.0444 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 35:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0198252  0.0003413   58.08 <0.0000000000000002 ***
## short_listTRUE -0.0061363  0.0003960  -15.49 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 36:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0061227  0.0003190  19.192 <0.0000000000000002 ***
## short_listTRUE 0.0041027  0.0004135   9.922 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 37:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0069696  0.0003209  21.717 < 0.0000000000000002 ***
## short_listTRUE 0.0014975  0.0004234   3.537             0.000405 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 38:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0226478  0.0005217   43.41 <0.0000000000000002 ***
## short_listTRUE -0.0073624  0.0005950  -12.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 39:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0205562  0.0006323  32.510 <0.0000000000000002 ***
## short_listTRUE 0.0011502  0.0008248   1.395               0.163    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 40:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0139283  0.0003594   38.75 <0.0000000000000002 ***
## short_listTRUE 0.0047452  0.0004551   10.43 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 41:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0107945  0.0001176   91.78 <0.0000000000000002 ***
## short_listTRUE -0.0014811  0.0001339  -11.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 42:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0150504  0.0004387  34.310 < 0.0000000000000002 ***
## short_listTRUE -0.0033592  0.0005544  -6.059        0.00000000138 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 43:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0140265  0.0004861  28.854 < 0.0000000000000002 ***
## short_listTRUE 0.0021683  0.0006085   3.563             0.000367 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 44:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0147186  0.0002651  55.512 < 0.0000000000000002 ***
## short_listTRUE 0.0016385  0.0003339   4.907          0.000000928 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 45:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0135442  0.0004527  29.917 < 0.0000000000000002 ***
## short_listTRUE -0.0036869  0.0005342  -6.902     0.00000000000517 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 46:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0067855  0.0003691  18.385 <0.0000000000000002 ***
## short_listTRUE 0.0042941  0.0004461   9.625 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 47:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0077717  0.0003512  22.131 < 0.0000000000000002 ***
## short_listTRUE 0.0027458  0.0004467   6.147       0.000000000793 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 48:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0225105  0.0005032   44.74 <0.0000000000000002 ***
## short_listTRUE -0.0077244  0.0005804  -13.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 49:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0155733  0.0004778  32.595 < 0.0000000000000002 ***
## short_listTRUE -0.0038787  0.0005826  -6.657       0.000000000028 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 50:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0232451  0.0006510   35.71 <0.0000000000000002 ***
## short_listTRUE -0.0076606  0.0007433  -10.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 51:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0105758  0.0003557   29.73 <0.0000000000000002 ***
## short_listTRUE 0.0055875  0.0004505   12.40 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 52:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.01749027 0.00018333  95.403 <0.0000000000000002 ***
## short_listTRUE 0.00001783 0.00022567   0.079               0.937    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 53:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0107597  0.0003954  27.214 <0.0000000000000002 ***
## short_listTRUE 0.0003058  0.0004669   0.655               0.513    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 54:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0261021  0.0004258  61.303 <0.0000000000000002 ***
## short_listTRUE -0.0044960  0.0004877  -9.219 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 55:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0101880  0.0001862  54.730 <0.0000000000000002 ***
## short_listTRUE -0.0004807  0.0002428  -1.979              0.0478 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 56:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0114028  0.0003551  32.109 < 0.0000000000000002 ***
## short_listTRUE -0.0015309  0.0004383  -3.493             0.000479 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 57:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0129042  0.0003259  39.591 <0.0000000000000002 ***
## short_listTRUE 0.0012948  0.0004148   3.121              0.0018 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 58:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0090822  0.0002441  37.205 < 0.0000000000000002 ***
## short_listTRUE 0.0015927  0.0003317   4.801           0.00000158 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 59:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0166452  0.0001943   85.65 < 0.0000000000000002 ***
## short_listTRUE 0.0007286  0.0002343    3.11              0.00187 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 60:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0100794  0.0003770  26.735 <0.0000000000000002 ***
## short_listTRUE -0.0002398  0.0004647  -0.516               0.606    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 61:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0112640  0.0001145   98.35 <0.0000000000000002 ***
## short_listTRUE 0.0019036  0.0001402   13.58 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 62:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0104597  0.0003952  26.468 <0.0000000000000002 ***
## short_listTRUE -0.0010403  0.0004701  -2.213              0.0269 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 63:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0088016  0.0003625  24.283 < 0.0000000000000002 ***
## short_listTRUE 0.0020139  0.0004635   4.345             0.000014 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 64:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0207819  0.0002268  91.627 < 0.0000000000000002 ***
## short_listTRUE 0.0017483  0.0002992   5.844        0.00000000513 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 65:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0136872  0.0003768  36.325 < 0.0000000000000002 ***
## short_listTRUE -0.0017374  0.0004535  -3.831             0.000128 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 66:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0132110  0.0004840  27.295 < 0.0000000000000002 ***
## short_listTRUE 0.0037754  0.0005862   6.441        0.00000000012 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 67:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0219834  0.0003086  71.241 < 0.0000000000000002 ***
## short_listTRUE -0.0011517  0.0003885  -2.965              0.00303 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 68:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0193971  0.0001786  108.61 <0.0000000000000002 ***
## short_listTRUE -0.0026874  0.0002288  -11.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 69:
## 
## Coefficients:
##                   Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     0.00275112  0.00002015 136.555 <0.0000000000000002 ***
## short_listTRUE -0.00002558  0.00002598  -0.985               0.325    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.3 Full topic model summary - K30

## A topic model with 30 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
##       Highest Prob: bill, amendment, clause, new, legislation, amendments, act 
##       FREX: amendment, clause, amendments, clauses, nos, insert, subsection 
##       Lift: #185, #85, 0.003, 05, 1-competences, 1-impact, 1,924 
##       Score: clause, amendment, amendments, bill, provisions, lords, nos 
## Topic 2 Top Words:
##       Highest Prob: issues, public, information, also, report, review, process 
##       FREX: consultation, review, guidance, recommendations, information, considering, decisions 
##       Lift: 1-who, 1,842, 109648, 1402, 151387, 1981-was, 1a-has 
##       Score: consultation, guidance, information, review, committee, issues, process 
## Topic 3 Top Words:
##       Highest Prob: women, men, pay, equality, rights, women's, discrimination 
##       FREX: women, equality, gender, equalities, bishops, discrimination, female 
##       Lift: gender, #112, #neverthelesshepersisted, 1-breast-feed, 1,087, 1,574, 1.57 
##       Score: women, women's, equality, men, gender, discrimination, girls 
## Topic 4 Top Words:
##       Highest Prob: police, crime, officers, behaviour, policing, home, antisocial 
##       FREX: policing, antisocial, constable, burglary, wardens, crime, constabulary 
##       Lift: 1,113, 1.24, 17,614, acpo's, adz, alcohol-free, alleygator 
##       Score: police, crime, officers, policing, antisocial, behaviour, constable 
## Topic 5 Top Words:
##       Highest Prob: european, uk, countries, eu, union, trade, international 
##       FREX: treaty, enlargement, wto, lisbon, doha, eu, eu's 
##       Lift: #420, 0.26, 0.56, 07, 09, 1-2, 1-of 
##       Score: eu, european, countries, treaty, armed, defence, forces 
## Topic 6 Top Words:
##       Highest Prob: transport, london, rail, bus, road, services, line 
##       FREX: rail, bus, passengers, fares, trains, buses, passenger 
##       Lift: #145, 0.1p, 0.45, 0.86, 1-very, 1,122, 1,658 
##       Score: rail, transport, bus, passengers, fares, trains, congestion 
## Topic 7 Top Words:
##       Highest Prob: people, work, benefit, pension, benefits, support, disabled 
##       FREX: disabled, jobcentre, incapacity, carers, pension, claimants, esa 
##       Lift: dla, #400, 0300, 1-to-1, 1,030, 1,052, 1,366 
##       Score: pension, carers, disabled, pensions, allowance, disability, credit 
## Topic 8 Top Words:
##       Highest Prob: immigration, safety, uk, asylum, enforcement, home, number 
##       FREX: dogs, dog, id, visa, fur, mink, hse 
##       Lift: 44a, a8, acoba, arcs, attachment-free, bareboat, bonfires 
##       Score: immigration, asylum, animals, dogs, fireworks, dog, animal 
## Topic 9 Top Words:
##       Highest Prob: health, research, cancer, treatment, medical, disease, can 
##       FREX: cancer, diseases, vaccine, flu, embryos, infections, diabetes 
##       Lift: 1169, 20-fold, ablation, abnormalities, adpkd, aed, anaesthesia 
##       Score: cancer, patients, disease, smoking, health, diagnosis, screening 
## Topic 10 Top Words:
##       Highest Prob: government, labour, conservative, party, opposition, policy, government's 
##       FREX: conservative, liberal, democrats, conservatives, scottish, democrat, scotland 
##       Lift: #nationalistsconfused, 1-but, 1.135, 10,182, 10.91, 1125, 116385 
##       Score: conservative, scottish, party, labour, government, scotland, liberal 
## Topic 11 Top Words:
##       Highest Prob: care, health, nhs, services, service, hospital, patients 
##       FREX: dentists, ambulance, dentistry, helier, dentist, nurses, hospital 
##       Lift: 2.24, 2005-6, 22,600, 422, 5.45pm, 8.03, 8.41 
##       Score: nhs, patients, care, hospital, health, patient, hospitals 
## Topic 12 Top Words:
##       Highest Prob: member, members, debate, house, mr, committee, said 
##       FREX: member, speaker, mr, debate, spoke, thoughtful, backbench 
##       Lift: e-petitions, @daisydumble, @percyblakeney63, 10,000-signature, 1028, 1080, 11.00 
##       Score: member, mr, committee, members, speaker, debate, house 
## Topic 13 Top Words:
##       Highest Prob: companies, financial, company, market, scheme, money, debt 
##       FREX: payday, annuity, oft, policyholders, penrose, fca, loan 
##       Lift: fca, oft, prepayment, #1.8, #20,000, 0.21, 0.84 
##       Score: companies, consumers, fsa, banks, company, customers, consumer 
## Topic 14 Top Words:
##       Highest Prob: young, people, health, mental, youth, prison, problems 
##       FREX: prisons, probation, cannabis, reoffending, mental, prison, self-harm 
##       Lift: cannabis, hawton, poppers, camhs, inmates, reoffending, #230 
##       Score: young, mental, prison, drugs, alcohol, youth, drug 
## Topic 15 Top Words:
##       Highest Prob: cases, court, legal, law, case, justice, evidence 
##       FREX: judicial, attorney-general, defendant, extradition, tpims, suspects, court 
##       Lift: 110-day, abscond, absconded, acquittals, adduce, anti-viral, babar 
##       Score: court, offence, courts, criminal, justice, prosecution, offences 
## Topic 16 Top Words:
##       Highest Prob: energy, businesses, business, jobs, investment, economy, industry 
##       FREX: carbon, renewable, renewables, solar, low-carbon, energy, feed-in 
##       Lift: fossil, sellafield, viyella, energy-intensive, low-carbon, #12.5, #140,000 
##       Score: energy, businesses, jobs, economy, manufacturing, industry, investment 
## Topic 17 Top Words:
##       Highest Prob: people, want, one, get, know, say, us 
##       FREX: things, think, something, get, want, going, really 
##       Lift: 1,027, 2.85, 30s-will, 6.37, 778, about-part, accept-there 
##       Score: people, get, think, things, going, want, say 
## Topic 18 Top Words:
##       Highest Prob: education, schools, school, children, training, skills, parents 
##       FREX: schools, teachers, pupils, curriculum, sen, academies, ofsted 
##       Lift: ema, #8,000, 1,000-pupil, 1,051, 1,100-i, 1,170, 1,204 
##       Score: schools, school, education, children, teachers, pupils, students 
## Topic 19 Top Words:
##       Highest Prob: constituency, city, people, many, years, work, centre 
##       FREX: fishermen, cod, hull, plymouth, maiden, fishing, fish 
##       Lift: #14.4, #66.6, 0.27, 0.51, 1,084, 1,126, 1.41 
##       Score: plymouth, constituency, hull, city, fishing, fish, arts 
## Topic 20 Top Words:
##       Highest Prob: housing, homes, people, private, london, social, home 
##       FREX: rent, tenants, landlords, rented, homelessness, homeless, rents 
##       Lift: right-to-buy, #19, #21.5, #28.5, 1,000-odd, 1,026, 1,083 
##       Score: housing, homes, rented, rent, tenants, landlords, affordable 
## Topic 21 Top Words:
##       Highest Prob: tax, year, million, government, budget, cuts, cut 
##       FREX: tax, obr, vat, millionaires, 50p, inflation, budget 
##       Lift: 0.38, 1,869, 107,500, 11.2, 13,600, 2,073, 2.33 
##       Score: tax, cuts, budget, poverty, chancellor, unemployment, billion 
## Topic 22 Top Words:
##       Highest Prob: food, post, office, rural, petition, offices, farmers 
##       FREX: petition, farmers, petitioners, meat, cull, labelling, cattle 
##       Lift: #450, 1072, 11,900, 12-point, 934, a690, ablewell 
##       Score: food, farmers, petitioners, petition, post, rural, offices 
## Topic 23 Top Words:
##       Highest Prob: people, international, human, government, war, rights, country 
##       FREX: syria, israel, civilians, palestinian, israeli, gaza, sri 
##       Lift: muslims, #aleppo, #no2lgbthate, 0.002, 1,000-almost, 1,010, 1,019 
##       Score: syria, un, israel, humanitarian, iraq, palestinian, israeli 
## Topic 24 Top Words:
##       Highest Prob: bbc, media, online, internet, sport, access, digital 
##       FREX: bbc, games, olympic, gambling, bbc's, copyright, lap-dancing 
##       Lift: age-restricted, age-verification, aquatics, bacta, bandwidth, bbfc, bduk 
##       Score: bbc, sport, tickets, internet, digital, online, football 
## Topic 25 Top Words:
##       Highest Prob: local, authorities, funding, areas, services, council, community 
##       FREX: local, authorities, funding, councils, grant, authority, formula 
##       Lift: 416,000, 596,000, 82-3, 885, allison's, baccy, bellwin 
##       Score: local, authorities, funding, councils, authority, council, services 
## Topic 26 Top Words:
##       Highest Prob: children, child, families, care, family, parents, violence 
##       FREX: trafficked, csa, same-sex, adopters, child, rape, marriages 
##       Lift: @mandatenow, 1-regardless, 1,000-discriminates, 1,142,600, 1,483, 1,746, 10-month-old 
##       Score: child, children, parents, violence, care, sexual, rape 
## Topic 27 Top Words:
##       Highest Prob: planning, water, development, land, environment, site, sites 
##       FREX: forestry, biodiversity, masts, habitats, gypsy, flood, waterways 
##       Lift: biodiversity, encampments, masts, #tartantories, 0fficial, 1,000-year-old, 1,251 
##       Score: planning, land, flood, marine, sites, water, site 
## Topic 28 Top Words:
##       Highest Prob: secretary, state, house, last, statement, report, said 
##       FREX: secretary, statement, state, confirm, official, answer, vol 
##       Lift: 12.40, ashleys, ayia, burne, cabinet's, cairns's, clutha 
##       Score: secretary, state, statement, answer, confirm, inquiry, leader 
## Topic 29 Top Words:
##       Highest Prob: parliament, wales, vote, commission, political, assembly, people 
##       FREX: electoral, polling, gibraltar, voting, assembly, vote, votes 
##       Lift: @leamingtonsbc, @maggieannehayes, @nhconsortium, #keeptweeting, 1-46, 1-would, 1,294 
##       Score: electoral, vote, elections, wales, assembly, referendum, welsh 
## Topic 30 Top Words:
##       Highest Prob: can, make, ensure, agree, important, take, made 
##       FREX: agree, aware, sure, ensure, taking, lady, welcome 
##       Lift: 1565, 19602, 2,095, 42931, 94254, agencies-an, anguish-filled 
##       Score: agree, aware, thank, ensure, point, lady, can

2.4 Full topic model estimate summary - K30

## 
## Call:
## estimateEffect(formula = 1:30 ~ short_list, stmobj = topic_model_k30, 
##     metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0449702  0.0006448  69.739 <0.0000000000000002 ***
## short_listTRUE -0.0068689  0.0008156  -8.422 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 2:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0757500  0.0006134  123.48 <0.0000000000000002 ***
## short_listTRUE -0.0215054  0.0007219  -29.79 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 3:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0230229  0.0006035  38.149 <0.0000000000000002 ***
## short_listTRUE -0.0007421  0.0006919  -1.073               0.283    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 4:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0289385  0.0006492  44.578 <0.0000000000000002 ***
## short_listTRUE -0.0074447  0.0007523  -9.896 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 5:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0368452  0.0006689  55.085 < 0.0000000000000002 ***
## short_listTRUE -0.0051017  0.0007838  -6.509      0.0000000000759 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 6:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0217916  0.0005761  37.829 < 0.0000000000000002 ***
## short_listTRUE 0.0054441  0.0007613   7.151    0.000000000000868 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 7:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0298344  0.0006489   45.97 <0.0000000000000002 ***
## short_listTRUE 0.0131773  0.0007897   16.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 8:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0186028  0.0004845  38.398 <0.0000000000000002 ***
## short_listTRUE 0.0004560  0.0006261   0.728               0.466    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 9:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0249526  0.0005936  42.033 < 0.0000000000000002 ***
## short_listTRUE -0.0051042  0.0007216  -7.073     0.00000000000153 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 10:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0378866  0.0004700  80.618 < 0.0000000000000002 ***
## short_listTRUE 0.0019999  0.0005925   3.375             0.000738 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 11:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0408025  0.0006947  58.736 <0.0000000000000002 ***
## short_listTRUE -0.0083331  0.0008785  -9.486 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 12:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0390032  0.0006049   64.47 <0.0000000000000002 ***
## short_listTRUE 0.0082105  0.0007367   11.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 13:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0302322  0.0006073   49.78 < 0.0000000000000002 ***
## short_listTRUE -0.0028580  0.0007663   -3.73             0.000192 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 14:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0239348  0.0005284  45.296 < 0.0000000000000002 ***
## short_listTRUE -0.0032166  0.0006376  -5.045          0.000000456 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 15:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0409453  0.0006981   58.66 <0.0000000000000002 ***
## short_listTRUE -0.0167479  0.0008565  -19.55 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 16:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0384177  0.0006692  57.406 < 0.0000000000000002 ***
## short_listTRUE -0.0046120  0.0008693  -5.305          0.000000113 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 17:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0788485  0.0005733 137.533 < 0.0000000000000002 ***
## short_listTRUE 0.0019968  0.0007196   2.775              0.00552 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 18:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0298568  0.0006882  43.381 < 0.0000000000000002 ***
## short_listTRUE 0.0037965  0.0008478   4.478           0.00000755 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 19:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0196088  0.0005073   38.65 <0.0000000000000002 ***
## short_listTRUE 0.0092533  0.0006687   13.84 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 20:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0204491  0.0005531  36.970 < 0.0000000000000002 ***
## short_listTRUE 0.0038428  0.0007524   5.107          0.000000328 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 21:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0460876  0.0008011   57.53 <0.0000000000000002 ***
## short_listTRUE 0.0142956  0.0010103   14.15 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 22:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0139586  0.0004706  29.661 <0.0000000000000002 ***
## short_listTRUE 0.0055231  0.0005725   9.647 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 23:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0261614  0.0006770  38.645 <0.0000000000000002 ***
## short_listTRUE 0.0007679  0.0008461   0.908               0.364    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 24:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0124067  0.0003947  31.436 <0.0000000000000002 ***
## short_listTRUE 0.0049008  0.0004951   9.899 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 25:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0426172  0.0006188   68.88 <0.0000000000000002 ***
## short_listTRUE -0.0085936  0.0007492  -11.47 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 26:
## 
## Coefficients:
##                   Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     0.02501676  0.00056573  44.220 <0.0000000000000002 ***
## short_listTRUE -0.00003169  0.00073090  -0.043               0.965    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 27:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0149391  0.0004739  31.523 <0.0000000000000002 ***
## short_listTRUE 0.0003263  0.0005921   0.551               0.582    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 28:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0427024  0.0005845   73.06 <0.0000000000000002 ***
## short_listTRUE 0.0145518  0.0007268   20.02 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 29:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0188018  0.0005255  35.777 <0.0000000000000002 ***
## short_listTRUE 0.0062939  0.0006902   9.118 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 30:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0526689  0.0003202 164.510 <0.0000000000000002 ***
## short_listTRUE -0.0037508  0.0003916  -9.577 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.5 AWS References to Constituents in Context

A random selection of 2% of all references to “my constituency”, “my constituent” and “my constituents”, by AWS MPs, in context.

A random sample of KWIC’s
Pre Keyword Post
Liverpool and many other places ? In one instance in my constituency , a rent officer accompanying a landlady , who was
not reduced . When I speak with young people in my constituency , I am often struck by the seriousness of the
? It rings very hollowly in Newcastle where many of my constituents cannot even plan for the next week , because they
pharmaceutical industry is a major contributor to our economy . My constituency is also home to the university of Hertfordshire , a
run its course . People running domestic violence refuges in my constituency are desperately worried that those refuges will not be there
my personal views and political instincts , and conflicted by my constituency’s large vote to remain and my country’s narrow vote to
a more local job . The Wigan area , which my constituency covers in part , is the part of the north-west
Deputy Speaker . Baroness Jay visited a local nursery in my constituency . We saw-it was fantastic-an independent nursery sharing premises with
other mothers and who are a particularly vulnerable group in my constituency ?
greater community participation  " to mean that people in my constituency will have a fairer say in developments and that they
n " , " Airedale general hospital is also in my constituency . It is the biggest employer in my constituency ,
Are the Government aware that many young Asian ladies in my constituency would like a change in immigration regulations to prevent those
that raising aspiration is crucial . It is crucial in my constituency , which suffered 18 years of under-investment under a Conservative
all of equal status . I cherish the diversity in my constituency and throughout the country . I am yet more determined
this year of 74 per cent . Another school in my constituency , however , has been in special measures , and
If it were to be used to help people in my constituency who are struggling with increased care costs and who are
" However , today I want to talk about how my constituency can once again make Britain great and how it can
one has had a particularly terrible impact on one of my constituents , Doris Armstrong , who has successfully seen him off
and SNP Members . I am talking about jobs in my constituency and not about independence , which is why I am
life chances agenda . I recently visited a school in my constituency , a primary school in Moss Side , that had
. I want to refer to the Pont valley in my constituency by way of illustration . It is a beautiful valley
was not included , and it is little comfort to my constituents around the River Cocker and in Winmarleigh and Thurnham ,
have greater-than-average difficulties with basic skills . A playwright from my constituency , Sue Ton , has brought her award-winning play "
made on planning last week . Can he explain why my constituents , who are now going to have to live 150
. What would my right hon . Friend say to my constituents about their fears ?
that pay a fair day’s wage . Weekly pay in my constituency is significantly below the national average , and the consequences
majority of the substantial and very diverse Muslim communities in my constituency are united in abhorring terrorism . Indeed , many members
the 1-2-3 formation . They were the best innovation in my constituency in the past 20 years . They tackle not only
form by far the largest part of my correspondence with my constituents . I have called a number of public meetings since
anywhere .  n " , " The diversity of my constituency is one of the reasons why it is the best
I hope that a strong message will go out to my constituents and other policyholders across the country of the deep seriousness
two valleys has attracted the television and film industry to my constituency . I am pleased about that because the television and
to your indulgence , and that of the House and my constituents , I was able to recover and return these past
My constituent who turned 60 this year has not received any information
One of my constituents who needs IVF treatment is in the unfortunate position of
Earlier in business questions , I raised the case of my constituents , Mike and Tina Trowhill . I had raised the
, including magnificent Ilkley , and its moor , in my constituency . The air ambulance helicopter is crucial for carrying patients
Royal Navy surface fleet . That has caused concern in my constituency-Portsmouth is the historic home of the Royal Navy-coming as it
them sleepless nights . Can my hon . Friend offer my constituents assurances that the Licensing Act will also address those problems
just winding up now .  n " , " My constituents are being asked to make sacrifices that they can ill
choose one’s lawyer has also been mentioned . One of my constituents , who is a solicitor , told me of a
is a particular concern . The town of Eastwood in my constituency faces the loss of its ambulance station . Does my
alternative arrangements . What assurances can the Prime Minister give my constituents that cuts to higher education will not affect students ?
there , and both the schools I attended are in my constituency . I am now a mum , bringing up my
Care Cymru , which is a local charity based in my constituency and which funds specialist cancer nurses to work closely with
on the closure of special schools , those children in my constituency would be denied their brand-new school and all the opportunities
British citizens of Kashmiri extraction have made their home in my constituency , and I take an interest on their behalf ,
in the future , but more than 4,000 pensioners in my constituency have benefited from the pension credit , with an average
. I had an email this morning from one of my constituents saying that her husband had taken his life on Friday
Three sub-post offices are earmarked for closure in my constituency , and one of them serves the most disadvantaged community
It seems , however , that at the very least my constituent has been given some very shoddy treatment , and it
is a design and creation company on Silicon roundabout in my constituency . It employs 65 people and recently produced a film
) : this is a pernicious and divisive measure . My constituents are saying to me , " Why am I being
enough pressure on young people trying to find work in my constituency . Figures last month showed an increase of 467 %
" Up to 1,000 households in my constituency face having their tax credits withdrawn this year , and
to open yet another children’s centre in the network in my constituency . They are doing extraordinarily important outreach work , particularly
thank the Secretary of State for his reply . In my constituency , 6,563 over 75-year-olds would benefit by £ 200 a
. For example , one of the major employers in my constituency-if not the single largest direct employer in our county-is a
violence against women . It concerns me that women in my constituency who have been victims of rape cannot gain access to
It is perhaps a reflection on my constituency that I have many constituents who have been caught up
Friend for her reply . A lot of people in my constituency still enjoy using cheques , which is a good thing
alarmed . I was clear that I did not want my constituency to be asset-stripped . Equally , I do not hold
forward some of my concerns about striking a balance . My constituents and , I imagine , quite a number of others
removed from schools in the Bradford district , which includes my constituency .
our prisons , nothing that happened in Winson Green in my constituency on Friday came as a shock . The independent monitoring
want to illustrate the other things that the plant in my constituency is doing . We have just secured £ 50 million
Friend the Member for Dudley , South . Many of my constituents from Brierley Hill , Amblecote and Quarry Bank learned to
The vicar at St Lawrence church in Darlaston in my constituency has to cover All Saints in Darlaston and All Saints
dissatisfaction . I also thank a number of groups in my constituency-Enfield Disability Action , Enfield transport users group and the Enfield
recognition , and I pay tribute to those people in my constituency and the officers on the council who have worked closely
takes less than half an hour for 83 % of my constituents to get to court .  n " )
on them already . It does not seem fair that my constituents should bear that burden . For that reason , I
will be reduced . Haulier companies such as Owens in my constituency , which employs 500 people , pay a massive amount
have a duty to legislate . I feel strongly about my constituents being conned-there is no other word for it . I
in Northampton need help with debt problems . Many of my constituents have no savings , but are sold more credit than
. It is privatisation that Londoners do not want . My constituents tell me that they want London Underground to stay in
there will be no social care in whole villages in my constituency . We are told that the Chancellor is minded to
will continue to push , because from the perspective of my constituency , where we have extraordinarily high house prices , if
did the same at Alma primary school-both of them in my constituency . I participated in a literacy hour lesson in both
appropriate to speak during the industry and environment debate since my constituency of Burnley is famed for its industrial legacy and its
. I know , from speaking to disability organisations in my constituency in Milton Keynes , that they very much appreciate the
local constituency MP . I was visited in Liverpool by my constituents Pat and Terry Hughes , who told me their story
since the very sad death of Jade Lomas Anderson in my constituency I have done a great deal of work on this
and organised crime , which are blighting many areas in my constituency and need to be tackled urgently . I also welcome
along with other measures , has cut significantly unemployment in my constituency . It has also enabled us to spend less on
sparsely populated areas are left out . In short , my constituents have been ill served by deregulation . For Sheffield ,
position . I have delivered petitions to this House from my constituents because bus services have been savagely cut in my constituency
credit unions will be supported and allowed to expand . My constituency has two excellent and progressive credit unions , Unify and
to consider how the regulator is responding to requests from my constituents , who have to wait until the gas supplier receives
such as bonuses . However , I have defended to my constituents the fact that if the Government had not stepped in
hon . Friend comment on the situation of one of my constituents , who murdered a man ? The murderer , my
under threat at present and the incomes of some of my constituents who are not involved in agriculture have already been reduced
? I attended last week with a bereaved family from my constituency , who were very impressed . Does my right hon
how difficult this is for real people . I asked my constituents how they are managing their energy costs , which we
On Friday I attended the opening of a micro-business in my constituency , Little Rembrandt’s Café in Ferryhill . That business opened
poor state of bus services in Tyne and Wear and my constituency . The Transport Act 1985 did not deliver on its
hon . Friend throw any light on that problem for my constituents ?
, there has been an overall fall in unemployment in my constituency of 37.7 per cent . If we had not lost
State’s comments in as far as they go . In my constituency , there is undoubtedly huge concern about the misuse of
, as well as the maximum number of jobs in my constituency ? Can he also tell the House what further he
the problems experienced with digital television reception in parts of my constituency . A constituent of mine , Mr . John Preece
changes are having in my constituency right now , and my constituents know what impact they are having . Action to tackle
and challenging it . I have taken the views of my constituents very seriously , but next week I intend to vote
some form of foundation course in the UK . In my constituency , Twin Training International Ltd provides such courses , along
In my constituency , 31 % of the children-more than 6,000 children -
ensure that the country is kept safe and secure . My constituents none the less have concerns , which I should like
the A and E department at Trafford General hospital in my constituency .  n " , " Over the years ,
and not to rely on discretionary payments , so that my constituents and others across the country can have a home that
leisure facility in one of the most beautiful parts of my constituency , even though no one wanted it or could afford
south Wales , with the losses in Llanwern , in my constituency , very much wrapped up in the announcement of the
improve the lives of a substantial number of people in my constituency . I also thought that it might illuminate and teach
but it is not the reality for too many of my constituents . We have seen a massive increase in precarious employment
county ? Ilkley is in a very beautiful part of my constituency , but the economic boom in Leeds and the excellent
Is my right hon . Friend aware that , in my constituency of Luton , South , the incidence of tuberculosis is
c ( " My constituent who has been campaigning on portability of care packages outwith
lot of work with a carers ’ support group in my constituency , Durham and Chester-le-Street Carers Support . Does my hon
are a matter of life and death for many of my constituents . I am therefore not surprised that more than 32,000
some issues , not least for Capel Manor college in my constituency , which is the only specialist horticultural college in London
our young people ?  " A secondary school in my constituency named after Admiral Lord Nelson has as its motto ,
The future of manufacturing industry in my constituency is in the high-tech , high-skilled industries . Would my
" , " I want to talk about tourism in my constituency . Tourism was mentioned earlier ; we know that VAT
A number of my constituents who have been given leave to remain in this country
, and also on access to jobs for many of my constituents , because when people factor in having to pay the
, " Where are the private sector jobs ? In my constituency and that of my hon . Friend the Member for
ones that surround it . I thought it important that my constituents ’ views about how their money is spent on their
in 1956 , and I know that manufacturing employers in my constituency are equally outraged by the Secretary of State’s proposals .
an excellent measure , and I know that many of my constituents will be pleased to see it come into force .
science and technology in this country . This is what my constituents are telling me . The hon . Gentleman can choose
Disorder Act 1998 . Some projects have been funded in my constituency by sources such as the neighbourhood renewal fund , and
c ( " Many of my constituents value highly the local radio station , BBC Radio Humberside-no
very aware that both Faye Turney and Arthur Batchelor are my constituents , so I was in close contact with the media
much power just in transmission . As a gentleman in my constituency says , it is always either windy , sunny or
not , among other things , ambassadors for Parliament ? My constituents cannot understand why we cannot introduce changes to use our
the businesses that create those jobs . Although many of my constituents are poor , they have no poverty of ambition ,
have experienced with planning applications for new housing developments in my constituency , which all too often are poorly designed , lack
" I welcome this opportunity to raise the concerns of my constituents who have difficulty in finding an NHS dentist . This
the decision . That will not meet the needs of my constituents , either , and I cannot support the police authority
the issues of dog welfare and dog control that put my constituents at risk ; and , worst of all , it
August 2006 , Luke Molnar , the 17-year-old son of my constituents Gill and Steve Molnar , died on the island of
have so many challenges across the country , including in my constituency ?
Act 2016 will do huge damage to my city and my constituency . It pulls the rug out from under Londoners on
. Friend will be aware that the two boroughs in my constituency , Westminster and Kensington and Chelsea , both suffered as
Let me tell the House about one hard-working family in my constituency that this Government are squeezing . Mark Thomas works for
social issues , which is important for the families in my constituency . I especially welcome the children’s tax credit , which
backgrounds , including poor backgrounds , and is representative of my constituency . That is the sort of school that Labour Members
. I recently saw that very clearly in Chirk in my constituency at what we will always think of as Cadbury’s ,
the London borough of Merton , half of which covers my constituency , dealing with incidents of graffiti has become increasingly challenging
Minister may not know or understand is that many of my constituents do not have cars or the money to take several
that subject certainly came up in the rural parts of my constituency . I have spoken in this place before about Broadband
I have described , and frustration on the part of my constituent , we received the letter dated 18 September that I
going to cope in the coming weeks and months . My constituency now has a food bank-the first ever in modern Oldham-and
c ( " No subject impacts more on my constituents than the cost of living . Wages are dropping-in the
we were in an interview recently . John Ruane from my constituency has a brain tumour , which means that he has
of Scotland in Partington-one of the most deprived areas of my constituency-has just announced closure plans . Will he ensure that there
has also contributed to the campaign . In Scotland , my constituent Rose Stallard has been campaigning on the matter . Recently
Carillion Energy , which is headquartered in my constituency , is undoubtedly keen to benefit from the Minister’s TLC
the acute hospitals to improve the hearing aid services for my constituents . My constituency is in an old industrial area where
and has continued to fail people in the area such my constituent , Lisa Welsh , and her neighbours , who have
HMRC office , with the loss of 60 staff . My constituent Sahin Kathawala has said that she may not even qualify
and take-up work , which has been well received in my constituency ? Will my hon . Friend confirm that this tax
My constituent Richard Freeman has not seen his son in six years
This morning , a constituent contacted my constituency office threatening to commit suicide because they were so depressed
who are working hard to improve the local communities in my constituency and the lives of local residents and I am proud
Part of my constituency had a bad experience with the Big Lottery Fund ,
, " I talked to Lordswood school for girls in my constituency , which still enters every pupil for at least one
Childminders in my constituency are very pro what they do and also pro the
of the poverty trap .  n " , " My constituents are virtually imprisoned by the excessively high rents charged for
to rectify the mistake . Without the food banks in my constituency , run by St George’s Crypt , St Bartholomew’s church
that I want to describe some of the problems that my constituents face . At present , unemployment stands at 10 per
more universal . I have had discussions with pensioners in my constituency , and a couple of other points arise out of
people who live in Slough . More than half of my constituents , if they marry someone from overseas , will be
was convicted in court in Llandudno last week of assaulting my constituent , Mr . Mark Aelwyn Roberts , causing him actual
There are very few local sources of employment , so my constituents from Little Hulton have to travel to get to work
, especially over the last 10 years and unemployment in my constituency is now at its lowest level for 10 years .
constituency . No job losses have occurred at Dewhirst in my constituency . Hopefully , with the help of the Assembly and
of individuals who have fallen foul of Concentrix . In my constituent’s case , her tax credits were cancelled while she was
that for myself when I am out and about in my constituency , and I am sure that the same applies to
school , where one of the teachers is another of my constituents-Mrs . Eileen Hosie . I have visited the school ,
of the country , but my understanding from schools in my constituency is that they are gaining no net benefit from the
with young children in pushchairs . Indeed , many of my constituents still hold on to the romantic notion of a return
fault of her own .  n " , " My constituent was of course very concerned , anxious and depressed about
creating the right environment to encourage investment . Corus in my constituency has up-to-date equipment and , at the home of the
just what a fantastic contribution he made to ensuring that my constituents knew that I was fighting on their behalf to keep
the naval base review , which has great significance for my constituency . Last year , Labour’s defence budget reached £ 30
bills , and high inflation more generally , many of my constituents are having to make painful savings in their household budgets
to stress from the outset that I do not believe my constituent’s experience is an isolated one , and wish to take
My constituents will be very disappointed that electrification will be starting in
to welcome the Minister with responsibility for rural communities to my constituency in the recess for a rural conference . I welcome
Last summer my constituent , Mr Kenneth Bailey , suffered a major stroke while
want to thank my hon . Friend for coming to my constituency recently to give a talk on the implications of independence
c ( " Absolutely . My constituency happens to have that tricky combination : short , steep
powers for PCSOs , who do an excellent job in my constituency and deserve the tools they need to do the job
proposals to change the sentencing guidelines . I know that my constituents whose three-year-old daughter was assaulted by Craig Sweeney will particularly
through her course , as are many young people in my constituency . I tell Ministers that the issue is pressing and
book .  "  n " , " In my constituency , the conspirators at Denby Poultry Products made hundreds of
issues that related to young people from my experience in my constituency . I visited a school and read out a poem
from the schools councils of all the secondary schools in my constituency . When I met them two weeks ago I expected
and say that in the small city of Dunblane in my constituency .
be aware that I have been campaigning for justice for my constituent , Michael Shields , who has now been transferred from
third sector IT training company for young people based in my constituency , told me of a young man who comes from
this approach , and I am sure that companies in my constituency will also do so , especially those operating in ship
a number of Remploy factories , including the one in my constituency . The local trade union rep from GMB has not
to rent a modest three-bedroom property in Finsbury Park in my constituency . This is unaffordable and represents a failure of the
intended to be up and running early in April ? My constituents who suffer from this disease want to know how to
Over the weekend , a number of my constituents have contacted me with concerns about the adequacy of security
it is perfectly possible that one of the wards in my constituency , East Ecclesfield , could end up being split into
proposals to close City pool by 2016 , and in my constituency to reduce funding for Newburn leisure centre while seeking alternative
£ 72,000 plus accommodation costs will not help many of my constituents , particularly those on lower or middle incomes .  
( John Robertson ) for the free ATM machines throughout my constituency , and I hope that there will soon be more
n " , " In her letter to me , my constituent said :  n " , "  " We
, there was the impact of women working . In my constituency , many women work , many in part-time jobs ,
credit . These are all incredibly valuable measures , and my constituents are talking about them and have high expectations from them
hon . Friend for that reply . For years , my constituents have been saying to me that they have saved for
a week for a GP appointment , and some in my constituency are waiting two weeks . A third cannot even get
Friend describes is also being experienced by GP practices in my constituency . GP practice managers have told me that the system
I welcome the Minister’s apology today , but how can my constituents and my police and crime commissioner and South Wales police
and Pensions about what it will do to ensure that my constituents and people everywhere get the support that they desperately need
of any borough in London , and the academies in my constituency are struggling . They have not delivered the soaring GCSE
make a great deal of difference to nurses working in my constituency . Does the Chancellor agree that it is in the
to the industry . The effect could be dramatic in my constituency . I only hope that the Chancellor has thought the
. The Park Wall North surface mine near Crook in my constituency is available for sale as part of a package consisting
, which is putting up our sugar prices and putting my constituents out of work . Can the Secretary of State assure
support for a substantial local application . My community and my constituents are thoroughly sick of the lack of support at national
difficult to fill vacancies . Nevertheless , many employers in my constituency have the mindset that older workers should not be considered
is in a Liberal Democrat constituency-but it serves half of my constituents . The health establishment has for many years scorned Mitcham

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  1. e.g. a reference to “the member for Bethnal Green and Bow” in keeping with Parliamentary convention of identifying MPs by their seat rather than their name would be followed by “(Rushnara Ali)”.

  2. Special Educational Needs